Data Reanalysis Assignment
Natalie Robinson
October 17, 2017
Female reproductive strategies in orangutans, evidence for female choice and counterstrategies to infanticide in a species with frequent sexual coercion
Cheryl D Knott, Melissa Emery Thompson, Rebecca M Stumpf and Matthew H McIntyre
The paper can be accessed here
Summary of Paper
Orangutans have exceptionally high rates of forced copulations. Traditionally, it was assumed that this was a strategy used by unflanged males, who are sexually mature, but have not developed secondary sexual characteristics like prime males who have large cheek flanges. Little thought was given to how female strategies may have shaped or responded to this behavior. Knott et al. (2010) found that male morph alone was not a good predictor of mating dynamics among wild Bornean orangutans (Pongo pygmaeus wurmbii), but rather female conception risk mediated the occurence of male-female interactions.
This is a flanged male orangutan named Codet.
This is an adult female, Delly, with her infant Duwyk.
Knott et al. (2010) assayed urine samples and compared ovarian hormone profiles with the sexual behavior of females and males in order to understand the females’ ovulatory cycles (as they do not have obvious sexual swellings like many other primates). Behavioral data was collected from 1994-2003 at the Cabang Panti Research Site in Gunung Palung National Park, West Kalimantan, Indonesia. During the 45,500 hours of observation, 387 encounters occured between males and females. Of these, male and female reproductive status could be determined for 153 encounters. 21 matings (between 10 males and 7 females) were observed during these encounters. They found that females were more likely to mate cooperatively with prime flanged males near ovulation, while they were more willing to associate and mate with non-prime males when conception risk was low.
Bornean orangutans live in lowland rainforests.
(all photos are from the Gunung Palung Conservation website)
In this reanalysis, I will attempt to replicate Figure 1, Figure 2, and Table 1. Figure 1 shows the distribution of matings with respect to male type and female ovulatory status. Figure 2 displays the distribution of female encounters with males, with respect to male type and female ovulatory status. Table 1 shows the results of the linear mixed model (LMM) analyses of female and male behaviors observed during copulations. These procedures will be described in more detail later on.
Required Packages
First, I loaded in the required packages.
library(curl)
library(plyr)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(stats)
library(AICcmodavg)
library(knitr)
library(broom)
library(graphics)
library(lme4)## Loading required package: Matrix##
## Attaching package: 'lme4'## The following object is masked from 'package:AICcmodavg':
##
## checkConvlibrary(car)##
## Attaching package: 'car'## The following object is masked from 'package:dplyr':
##
## recodelibrary(lmerTest)##
## Attaching package: 'lmerTest'## The following object is masked from 'package:lme4':
##
## lmer## The following object is masked from 'package:stats':
##
## stepObtaining the Data
I first recieved this data in various files of .sav format, as it had been exported from SPSS. I was able to open the files in R using the “foreign” package and the command read.spss, though I decided it would be easier to manipulate the data if it were in a .csv file, as I am more familiar with this format.
I was able to convert the files online using this website where you can upload a .sav file and then download the corresponding .csv file. All of the converted files are available in my Github repository titled “data-reanalysis-assignment.” First, I loaded in the main csv file, which contains all of the compiled data.
# getting the data into R
library(curl)
f <- curl("https://raw.githubusercontent.com/natalierobinson96/data-reanalysis-assignment/master/matings%20with%20endocrine%20data.csv")
d <- read.csv(f, header = TRUE, sep = ",")
head(d)## Date day year year2 DateSPSS Name Name2 Record.
## 1 33193 321 90 90.87748 11/17/90 Kristen Kristen 7
## 2 33194 322 90 90.88022 11/18/90 Kristen Kristen 8
## 3 33194 322 90 90.88022 11/18/90 Kristen Kristen 9
## 4 33248 10 91 91.02806 1/10/91 Female A Afemale 10
## 5 33257 19 91 91.05270 1/19/91 Beth Beth 11
## 6 33878 275 92 92.75291 10/1/92 Marissa Marissa 20
## TypeofInteraction FemaleAgeclass FemaleMotherClass OffspringClass
## 1 Mating 1 Adult Female Mother Over 4 yrs
## 2 Mating 1 Adult Female Mother Over 4 yrs
## 3 Mating 2 Adult Female Mother Over 4 yrs
## 4 Mating 1 Adult Female Non-mother No infant
## 5 Mating 1 Adult Female Non-mother No infant
## 6 Mating 1 Adult Female Mother Over 4 yrs
## ObservedReproductiveStatus EndocrineStatusHormones CyclePhaseHormones
## 1 Pregnant
## 2 Pregnant
## 3 Pregnant
## 4 Pregnant
## 5 Pregnant
## 6 L7
## ReproductiveStatus Endo2 Endo3 Endo4 Endo5 Endo6
## 1 Pregnant non-cycling Pregnant Pregnant Pregnant Pregnant
## 2 Pregnant non-cycling Pregnant Pregnant Pregnant Pregnant
## 3 Pregnant non-cycling Pregnant Pregnant Pregnant Pregnant
## 4 Pregnant non-cycling Pregnant Pregnant Pregnant Pregnant
## 5 Pregnant non-cycling Pregnant Pregnant Pregnant Pregnant
## 6 Cycling non-POP non-POP non-POP non-POP non-POP
## Endo7 Endo8 Pregnantvs.nonpregnant E1CmgCr PDGmgCr CommentMET
## 1 non-POP Pregnant Pregnant NA NA NA
## 2 non-POP Pregnant Pregnant NA NA NA
## 3 non-POP Pregnant Pregnant NA NA NA
## 4 non-POP Pregnant Pregnant NA NA NA
## 5 non-POP Pregnant Pregnant NA NA NA
## 6 non-POP non-POP Non-Preg NA NA NA
## EndorineComment
## 1 Pregnant as of 22 Nov 94, no sample this date: could be pregnant already or conceptive cycle
## 2 Pregnant as of 22 Nov 94, no sample this date: could be pregnant already or conceptive cycle
## 3 Pregnant as of 22 Nov 94, no sample this date: could be pregnant already or conceptive cycle
## 4 Pregnant based on CK\032s analysis of E1C
## 5 High E & P
## 6 ovulation occurs 15-17 Oct -- see attached
## SocialInteraction InteractionwithMale Mating NearMating Consortship
## 1 Y Y Y N Y
## 2 Y Y Y N Y
## 3 Y Y Y N Y
## 4 Y Y Y N Y
## 5 Y Y Y Y
## 6 Y Y Y N N
## ObservationMinutes FollowType FollowTypeII Lengthofmating
## 1 NA N No data 0.009028
## 2 424 H lost M 0.004861
## 3 424 H lost M 0.021528
## 4 617 H lost H 0.004861
## 5 743 F full F 0.015972
## 6 281 D found then full day D 0.006250
## PelvicThrusts Male MaletypeI MaleTypeII MaleTypeIII
## 1 NA Male V Unflanged Unflanged Non-Prime
## 2 NA Male V Unflanged Unflanged Non-Prime
## 3 NA Male V Unflanged Unflanged Non-Prime
## 4 NA Subadult T Unflanged Unflanged Non-Prime
## 5 NA Phil Unflanged Unflanged Non-Prime
## 6 NA UML02Oct96 Unflanged Unflanged Non-Prime
## MatingTypeExcel MatingTypePercent MatingTypePercentII MatingTypeII
## 1 Cooperative Cooperative Cooperative Unforced
## 2 Cooperative No Data Cooperative Unforced
## 3 Cooperative Cooperative Cooperative Unforced
## 4 Cooperative Cooperative Cooperative Unforced
## 5 Cooperative Cooperative Cooperative Unforced
## 6 Forced Forced Forced Forced
## MatingTypeIII Matingtypequestionable
## 1 Proceptive No
## 2 Unresisted No
## 3 Proceptive No
## 4 Unresisted No
## 5 Proceptive No
## 6 Resisted No
## TotalnonresistantProceptiveandResistanceBehaviors
## 1 2
## 2 NA
## 3 2
## 4 1
## 5 4
## 6 2
## NonResistantBeforeBehaviors NonResistantDuringBehaviors
## 1 1 NA
## 2 NA NA
## 3 1 NA
## 4 1 NA
## 5 NA NA
## 6 NA NA
## Totalnonresistantbehaviors NonresistantBehaviors ProceptiveBehaviors
## 1 1 0.5 1
## 2 NA NA NA
## 3 1 0.5 1
## 4 1 1.0 NA
## 5 NA 0.0 4
## 6 NA 0.0 NA
## ProceptiveBehaviors_A TotalunresistedandProcepetivebehaviors
## 1 0.5 2
## 2 NA NA
## 3 0.5 2
## 4 0.0 1
## 5 1.0 4
## 6 0.0 NA
## unresistedandProcepetivebehaviors ResistantBeforeBehaviors
## 1 1 NA
## 2 NA NA
## 3 1 NA
## 4 1 NA
## 5 1 NA
## 6 0 NA
## BeforeBehaviors ResistantDuringBehaviors DuringBehaviors
## 1 0 NA 0
## 2 NA NA NA
## 3 0 NA 0
## 4 0 NA 0
## 5 0 NA 0
## 6 0 2 1
## TotalResistantBehaviors ResistantBehaviors AttractionBehaviors Mating01
## 1 NA 0 1 1
## 2 NA NA NA 1
## 3 NA 0 NA 1
## 4 NA 0 1 1
## 5 NA 0 NA 1
## 6 2 1 1 1
## POP preg lact prime past unflanged included endomiss
## 1 0 1 0 0 0 1 1 0
## 2 0 1 0 0 0 1 1 0
## 3 0 1 0 0 0 1 1 0
## 4 0 1 0 0 0 1 1 0
## 5 0 1 0 0 0 1 1 0
## 6 0 0 0 0 0 1 1 0This file contains the follow records in which matings occurred, and both male and female reproductive status were known. Some of the most important columns list: the female reproductive status, the male type, whether there was a social interaction, whether this interaction was with a male, and whether a mating occured.
Figure 1 (Descriptive)
First I replicated Figure 1 from the paper. This figure shows the distribution of matings with respect to male type and female ovulatory status.
I began by creating 3 variables of female mating type. Each female was either pregnant, in the periovulatory period, or neither (non-periovulatory).
nonPOP <- filter(d, preg == "0" & POP == "0")
nonPOP #matings with females not in periovulatory period; currently cycling## Date day year year2 DateSPSS Name Name2 Record.
## 1 33878 275 92 92.75291 10/1/92 Marissa Marissa 20
## 2 34061 93 93 93.25394 4/3/93 Marissa Marissa 22
## 3 34340 7 94 94.01780 1/7/94 Elizabeth Elizabeth 31
## 4 34343 10 94 94.02601 1/10/94 Zarina Zarina 35
## 5 34344 11 94 94.02875 1/11/94 Zarina Zarina 41
## 6 36210 50 99 99.13758 2/19/99 Kristen Kristen 78
## TypeofInteraction FemaleAgeclass FemaleMotherClass OffspringClass
## 1 Mating 1 Adult Female Mother Over 4 yrs
## 2 Mating 1 Adult Female Mother Over 4 yrs
## 3 Mating 1 Adult Female Mother Inf no juv
## 4 Mating 1 Adult Female Mother Over 4 yrs
## 5 Mating 1 Adult Female Mother Over 4 yrs
## 6 Mating 1 Adult Female Mother Inf ind juv
## ObservedReproductiveStatus EndocrineStatusHormones CyclePhaseHormones
## 1 L7
## 2 L8
## 3 L4
## 4
## 5
## 6 L1
## ReproductiveStatus Endo2 Endo3 Endo4 Endo5 Endo6 Endo7
## 1 Cycling non-POP non-POP non-POP non-POP non-POP non-POP
## 2 Cycling non-POP non-POP non-POP non-POP non-POP non-POP
## 3 Cycling non-POP Cycling POP POP Near-POP POP
## 4 Cycling non-POP non-POP non-POP non-POP non-POP non-POP
## 5 Cycling non-POP non-POP non-POP non-POP non-POP non-POP
## 6 Lactating non-cycling non-POP non-POP non-POP Lactating No Data
## Endo8 Pregnantvs.nonpregnant E1CmgCr PDGmgCr CommentMET
## 1 non-POP Non-Preg NA NA NA
## 2 non-POP Non-Preg NA NA NA
## 3 non-POP Non-Preg NA NA NA
## 4 non-POP Non-Preg NA NA NA
## 5 non-POP Non-Preg NA NA NA
## 6 No Data Non-Preg NA NA NA
## EndorineComment
## 1 ovulation occurs 15-17 Oct -- see attached
## 2 mid-luteal phase
## 3 High P 6 Jan-16 Jan, no data when E peak occurred, looks just after ovulation
## 4 Ovulation occurs between 1/16 and 1/22
## 5 Ovulation occurs between 1/16 and 1/22
## 6
## SocialInteraction InteractionwithMale Mating NearMating Consortship
## 1 Y Y Y N N
## 2 Y Y Y N N
## 3 Y Y Y N Y
## 4 Y Y Y N Y
## 5 Y Y Y N Y
## 6 Y Y Y N N
## ObservationMinutes FollowType FollowTypeII Lengthofmating
## 1 281 D found then full day D 0.006250
## 2 700 F full F 0.006053
## 3 785 L late full day F 0.001389
## 4 815 F full F 0.001273
## 5 820 F full F 0.003299
## 6 253 D found then full day 0.002894
## PelvicThrusts Male MaletypeI MaleTypeII MaleTypeIII
## 1 NA UML02Oct96 Unflanged Unflanged Non-Prime
## 2 NA UML03Apr97 Unflanged Unflanged Non-Prime
## 3 10 Gagung Unflanged Unflanged Non-Prime
## 4 NA Toby Unflanged Unflanged Non-Prime
## 5 98 Toby Unflanged Unflanged Non-Prime
## 6 NA Roman Flanged Past Prime Non-Prime
## MatingTypeExcel MatingTypePercent MatingTypePercentII MatingTypeII
## 1 Forced Forced Forced Forced
## 2 Forced Forced Forced Forced
## 3 Cooperative Cooperative Cooperative Unforced
## 4 Cooperative Cooperative Cooperative Unforced
## 5 Forced Forced Forced Forced
## 6 Forced Forced Forced Unforced
## MatingTypeIII Matingtypequestionable
## 1 Resisted No
## 2 Resisted No
## 3 Unresisted No
## 4 Unresisted No
## 5 Resisted No
## 6 Unresisted Yes
## TotalnonresistantProceptiveandResistanceBehaviors
## 1 2
## 2 4
## 3 3
## 4 1
## 5 2
## 6 5
## NonResistantBeforeBehaviors NonResistantDuringBehaviors
## 1 NA NA
## 2 NA 1
## 3 1 NA
## 4 1 NA
## 5 NA NA
## 6 NA 1
## Totalnonresistantbehaviors NonresistantBehaviors ProceptiveBehaviors
## 1 NA 0.00 NA
## 2 1 0.25 NA
## 3 1 0.33 1
## 4 1 1.00 NA
## 5 NA 0.00 NA
## 6 1 0.20 NA
## ProceptiveBehaviors_A TotalunresistedandProcepetivebehaviors
## 1 0.00 NA
## 2 0.00 1
## 3 0.33 2
## 4 0.00 1
## 5 0.00 NA
## 6 0.00 1
## unresistedandProcepetivebehaviors ResistantBeforeBehaviors
## 1 0.00 NA
## 2 0.25 1
## 3 0.67 1
## 4 1.00 NA
## 5 0.00 1
## 6 0.20 4
## BeforeBehaviors ResistantDuringBehaviors DuringBehaviors
## 1 0.00 2 1.0
## 2 0.25 2 0.5
## 3 0.33 NA 0.0
## 4 0.00 NA 0.0
## 5 0.50 1 0.5
## 6 0.80 NA 0.0
## TotalResistantBehaviors ResistantBehaviors AttractionBehaviors Mating01
## 1 2 1.00 1 1
## 2 3 0.75 NA 1
## 3 1 0.33 3 1
## 4 NA 0.00 NA 1
## 5 2 1.00 1 1
## 6 4 0.80 1 1
## POP preg lact prime past unflanged included endomiss
## 1 0 0 0 0 0 1 1 0
## 2 0 0 0 0 0 1 1 0
## 3 0 0 0 0 0 1 1 0
## 4 0 0 0 0 0 1 1 0
## 5 0 0 0 0 0 1 1 0
## 6 0 0 1 0 1 0 1 0pregnant <- filter(d, preg == "1")
pregnant #matings with pregnant females ## Date day year year2 DateSPSS Name Name2 Record.
## 1 33193 321 90 90.87748 11/17/90 Kristen Kristen 7
## 2 33194 322 90 90.88022 11/18/90 Kristen Kristen 8
## 3 33194 322 90 90.88022 11/18/90 Kristen Kristen 9
## 4 33248 10 91 91.02806 1/10/91 Female A Afemale 10
## 5 33257 19 91 91.05270 1/19/91 Beth Beth 11
## 6 35764 335 97 97.91650 12/1/97 Kayla Kayla 73
## 7 35765 336 97 97.91923 12/2/97 Kayla Kayla 75
## 8 35766 337 97 97.92197 12/3/97 Kayla Kayla 76
## TypeofInteraction FemaleAgeclass FemaleMotherClass OffspringClass
## 1 Mating 1 Adult Female Mother Over 4 yrs
## 2 Mating 1 Adult Female Mother Over 4 yrs
## 3 Mating 2 Adult Female Mother Over 4 yrs
## 4 Mating 1 Adult Female Non-mother No infant
## 5 Mating 1 Adult Female Non-mother No infant
## 6 Mating 1 Adult Female Non-mother No infant
## 7 Mating 1 Adult Female Non-mother No infant
## 8 Mating 1 Adult Female Non-mother No infant
## ObservedReproductiveStatus EndocrineStatusHormones CyclePhaseHormones
## 1 Pregnant
## 2 Pregnant
## 3 Pregnant
## 4 Pregnant
## 5 Pregnant
## 6 Pregnant
## 7 Pregnant
## 8 Pregnant cycling Not sure
## ReproductiveStatus Endo2 Endo3 Endo4 Endo5 Endo6
## 1 Pregnant non-cycling Pregnant Pregnant Pregnant Pregnant
## 2 Pregnant non-cycling Pregnant Pregnant Pregnant Pregnant
## 3 Pregnant non-cycling Pregnant Pregnant Pregnant Pregnant
## 4 Pregnant non-cycling Pregnant Pregnant Pregnant Pregnant
## 5 Pregnant non-cycling Pregnant Pregnant Pregnant Pregnant
## 6 Pregnant non-cycling Pregnant Pregnant Pregnant Pregnant
## 7 Pregnant non-cycling Pregnant Pregnant Pregnant Pregnant
## 8 Pregnant non-cycling Pregnant Pregnant Pregnant Pregnant
## Endo7 Endo8 Pregnantvs.nonpregnant E1CmgCr PDGmgCr CommentMET
## 1 non-POP Pregnant Pregnant NA NA NA
## 2 non-POP Pregnant Pregnant NA NA NA
## 3 non-POP Pregnant Pregnant NA NA NA
## 4 non-POP Pregnant Pregnant NA NA NA
## 5 non-POP Pregnant Pregnant NA NA NA
## 6 non-POP Pregnant Pregnant NA NA NA
## 7 non-POP Pregnant Pregnant NA NA NA
## 8 non-POP Pregnant Pregnant NA 2275 NA
## EndorineComment
## 1 Pregnant as of 22 Nov 94, no sample this date: could be pregnant already or conceptive cycle
## 2 Pregnant as of 22 Nov 94, no sample this date: could be pregnant already or conceptive cycle
## 3 Pregnant as of 22 Nov 94, no sample this date: could be pregnant already or conceptive cycle
## 4 Pregnant based on CK\032s analysis of E1C
## 5 High E & P
## 6
## 7
## 8
## SocialInteraction InteractionwithMale Mating NearMating Consortship
## 1 Y Y Y N Y
## 2 Y Y Y N Y
## 3 Y Y Y N Y
## 4 Y Y Y N Y
## 5 Y Y Y Y
## 6 Y Y Y Y Y
## 7 Y Y Y N Y
## 8 Y Y Y N Y
## ObservationMinutes FollowType FollowTypeII Lengthofmating
## 1 NA N No data 0.009028
## 2 424 H lost M 0.004861
## 3 424 H lost M 0.021528
## 4 617 H lost H 0.004861
## 5 743 F full F 0.015972
## 6 285 L late full day 0.006944
## 7 880 F full F 0.008333
## 8 815 F full F 0.002083
## PelvicThrusts Male MaletypeI MaleTypeII MaleTypeIII
## 1 NA Male V Unflanged Unflanged Non-Prime
## 2 NA Male V Unflanged Unflanged Non-Prime
## 3 NA Male V Unflanged Unflanged Non-Prime
## 4 NA Subadult T Unflanged Unflanged Non-Prime
## 5 NA Phil Unflanged Unflanged Non-Prime
## 6 374 Wendell Flanged Prime Male Prime
## 7 384 Wendell Flanged Prime Male Prime
## 8 NA Wendell Flanged Prime Male Prime
## MatingTypeExcel MatingTypePercent MatingTypePercentII MatingTypeII
## 1 Cooperative Cooperative Cooperative Unforced
## 2 Cooperative No Data Cooperative Unforced
## 3 Cooperative Cooperative Cooperative Unforced
## 4 Cooperative Cooperative Cooperative Unforced
## 5 Cooperative Cooperative Cooperative Unforced
## 6 Cooperative Cooperative Cooperative Unforced
## 7 Cooperative Cooperative Cooperative Unforced
## 8 Cooperative Cooperative Unforced
## MatingTypeIII Matingtypequestionable
## 1 Proceptive No
## 2 Unresisted No
## 3 Proceptive No
## 4 Unresisted No
## 5 Proceptive No
## 6 Proceptive No
## 7 Proceptive No
## 8 Proceptive No
## TotalnonresistantProceptiveandResistanceBehaviors
## 1 2
## 2 NA
## 3 2
## 4 1
## 5 4
## 6 4
## 7 7
## 8 5
## NonResistantBeforeBehaviors NonResistantDuringBehaviors
## 1 1 NA
## 2 NA NA
## 3 1 NA
## 4 1 NA
## 5 NA NA
## 6 NA NA
## 7 NA NA
## 8 NA NA
## Totalnonresistantbehaviors NonresistantBehaviors ProceptiveBehaviors
## 1 1 0.5 1
## 2 NA NA NA
## 3 1 0.5 1
## 4 1 1.0 NA
## 5 NA 0.0 4
## 6 NA 0.0 4
## 7 NA 0.0 7
## 8 NA 0.0 5
## ProceptiveBehaviors_A TotalunresistedandProcepetivebehaviors
## 1 0.5 2
## 2 NA NA
## 3 0.5 2
## 4 0.0 1
## 5 1.0 4
## 6 1.0 4
## 7 1.0 7
## 8 1.0 5
## unresistedandProcepetivebehaviors ResistantBeforeBehaviors
## 1 1 NA
## 2 NA NA
## 3 1 NA
## 4 1 NA
## 5 1 NA
## 6 1 NA
## 7 1 NA
## 8 1 NA
## BeforeBehaviors ResistantDuringBehaviors DuringBehaviors
## 1 0 NA 0
## 2 NA NA NA
## 3 0 NA 0
## 4 0 NA 0
## 5 0 NA 0
## 6 0 NA 0
## 7 0 NA 0
## 8 0 NA 0
## TotalResistantBehaviors ResistantBehaviors AttractionBehaviors Mating01
## 1 NA 0 1 1
## 2 NA NA NA 1
## 3 NA 0 NA 1
## 4 NA 0 1 1
## 5 NA 0 NA 1
## 6 NA 0 2 1
## 7 NA 0 5 1
## 8 NA 0 NA 1
## POP preg lact prime past unflanged included endomiss
## 1 0 1 0 0 0 1 1 0
## 2 0 1 0 0 0 1 1 0
## 3 0 1 0 0 0 1 1 0
## 4 0 1 0 0 0 1 1 0
## 5 0 1 0 0 0 1 1 0
## 6 0 1 0 1 0 0 1 0
## 7 0 1 0 1 0 0 1 0
## 8 0 1 0 1 0 0 1 0POP <- filter(d, POP == "1")
POP #matings with females in periovulatory period## Date day year year2 DateSPSS Name Name2 Record.
## 1 34318 350 93 93.95756 12/16/93 Marissa Marissa 26
## 2 34318 350 93 93.95756 12/16/93 Marissa Marissa 27
## 3 34340 7 94 94.01780 1/7/94 Marissa Marissa 30
## 4 34341 8 94 94.02053 1/8/94 Marissa Marissa 32
## 5 34343 10 94 94.02601 1/10/94 Marissa Marissa 39
## 6 34347 14 94 94.03696 1/14/94 Zarina Zarina 42
## 7 34349 16 94 94.04244 1/16/94 Zarina Zarina 45
## TypeofInteraction FemaleAgeclass FemaleMotherClass OffspringClass
## 1 Mating 1 Adult Female Mother Over 4 yrs
## 2 Mating 2 Adult Female Mother Over 4 yrs
## 3 Mating 1 Adult Female Mother Over 4 yrs
## 4 Mating 1 Adult Female Mother Over 4 yrs
## 5 Mating 1 Adult Female Mother Over 4 yrs
## 6 Mating 1 Adult Female Mother Over 4 yrs
## 7 Mating 1 Adult Female Mother Over 4 yrs
## ObservedReproductiveStatus EndocrineStatusHormones CyclePhaseHormones
## 1 L8 cycling pop
## 2 L8 cycling pop
## 3
## 4
## 5 cycling near-pop
## 6
## 7
## ReproductiveStatus Endo2 Endo3 Endo4 Endo5 Endo6 Endo7
## 1 Cycling POP Cycling POP POP POP POP
## 2 Cycling POP Cycling POP POP POP POP
## 3 Cycling Near-POP Cycling POP non-POP Near-POP non-POP
## 4 Cycling Near-POP Cycling POP non-POP Near-POP non-POP
## 5 Cycling Near-POP Cycling POP non-POP Near-POP non-POP
## 6 Cycling POP Cycling POP POP POP POP
## 7 Cycling POP Cycling POP POP POP POP
## Endo8 Pregnantvs.nonpregnant E1CmgCr PDGmgCr CommentMET
## 1 POP Non-Preg 46754 1249 NA
## 2 POP Non-Preg 46754 1249 NA
## 3 non-POP Non-Preg NA NA NA
## 4 non-POP Non-Preg NA NA NA
## 5 non-POP Non-Preg 65153 891 NA
## 6 POP Non-Preg NA NA NA
## 7 POP Non-Preg NA NA NA
## EndorineComment SocialInteraction
## 1 ovulation occurs approx 15-16 Dec see attached Y
## 2 ovulation occurs approx 15-16 Dec see attached Y
## 3 ovulation should occur approx 15-17 Jan see attached Y
## 4 ovulation should occur approx 15-17 Jan see attached Y
## 5 ovulation should occur approx 15-17 Jan see attached Y
## 6 Ovulation occurs between 1/16 and 1/22 Y
## 7 Ovulation occurs between 1/16 and 1/22 Y
## InteractionwithMale Mating NearMating Consortship ObservationMinutes
## 1 Y Y Y Y 667
## 2 Y Y Y Y 667
## 3 Y Y Y N 806
## 4 Y Y Y Y 792
## 5 Y Y Y Y 770
## 6 Y Y N Y 795
## 7 Y Y Y Y 648
## FollowType FollowTypeII Lengthofmating PelvicThrusts Male
## 1 F full F 0.003785 NA Jari Manis
## 2 F full F 0.004074 NA Jari Manis
## 3 F full F 0.002743 NA Jari Manis
## 4 F full F 0.010266 NA Jari Manis
## 5 F full F 0.001910 224 Jari Manis
## 6 F full F 0.004086 261 Roman
## 7 H lost 0.009028 113 Jari Manis
## MaletypeI MaleTypeII MaleTypeIII MatingTypeExcel MatingTypePercent
## 1 Flanged Prime Male Prime Cooperative Cooperative
## 2 Flanged Prime Male Prime Cooperative Cooperative
## 3 Flanged Prime Male Prime Forced Forced
## 4 Flanged Prime Male Prime Forced Cooperative
## 5 Flanged Prime Male Prime Forced Forced
## 6 Flanged Past Prime Non-Prime Forced Forced
## 7 Flanged Prime Male Prime Cooperative Cooperative
## MatingTypePercentII MatingTypeII MatingTypeIII Matingtypequestionable
## 1 Cooperative Unforced Unresisted No
## 2 Cooperative Unforced Unresisted No
## 3 Forced Forced Resisted No
## 4 Cooperative Unforced Unresisted Yes
## 5 Forced Forced Resisted No
## 6 Forced Forced Resisted No
## 7 Cooperative Unforced Proceptive No
## TotalnonresistantProceptiveandResistanceBehaviors
## 1 1
## 2 2
## 3 3
## 4 3
## 5 3
## 6 2
## 7 6
## NonResistantBeforeBehaviors NonResistantDuringBehaviors
## 1 1 NA
## 2 NA 1
## 3 NA 1
## 4 1 1
## 5 NA 1
## 6 NA NA
## 7 NA NA
## Totalnonresistantbehaviors NonresistantBehaviors ProceptiveBehaviors
## 1 1 1.00 NA
## 2 1 0.50 NA
## 3 1 0.33 NA
## 4 2 0.67 NA
## 5 1 0.33 NA
## 6 NA 0.00 NA
## 7 NA 0.00 6
## ProceptiveBehaviors_A TotalunresistedandProcepetivebehaviors
## 1 0 1
## 2 0 1
## 3 0 1
## 4 0 2
## 5 0 1
## 6 0 NA
## 7 1 6
## unresistedandProcepetivebehaviors ResistantBeforeBehaviors
## 1 1.00 NA
## 2 0.50 1
## 3 0.33 1
## 4 0.67 1
## 5 0.33 1
## 6 0.00 NA
## 7 1.00 NA
## BeforeBehaviors ResistantDuringBehaviors DuringBehaviors
## 1 0.00 NA 0.00
## 2 0.50 NA 0.00
## 3 0.33 1 0.33
## 4 0.33 NA 0.00
## 5 0.33 1 0.33
## 6 0.00 2 1.00
## 7 0.00 NA 0.00
## TotalResistantBehaviors ResistantBehaviors AttractionBehaviors Mating01
## 1 NA 0.00 2 1
## 2 1 0.50 3 1
## 3 2 0.67 2 1
## 4 1 0.33 1 1
## 5 2 0.67 3 1
## 6 2 1.00 2 1
## 7 NA 0.00 NA 1
## POP preg lact prime past unflanged included endomiss
## 1 1 0 0 1 0 0 1 0
## 2 1 0 0 1 0 0 1 0
## 3 1 0 0 1 0 0 1 0
## 4 1 0 0 1 0 0 1 0
## 5 1 0 0 1 0 0 1 0
## 6 1 0 0 0 1 0 1 0
## 7 1 0 0 1 0 0 1 0Next I created 6 more variables which will serve as each entry of the replicated bar plot. The 3 female ovulatory statuses stay the same, though they are divided bewtween those who mated with prime males and those who mated with non-prime males (either unflanged or past-prime). For each variable I also counted the number of occurences of the type of mating by counting the number of rows.
nonPOP_nonprime <- filter(nonPOP, prime == "0")
nonPOP_nonprime## Date day year year2 DateSPSS Name Name2 Record.
## 1 33878 275 92 92.75291 10/1/92 Marissa Marissa 20
## 2 34061 93 93 93.25394 4/3/93 Marissa Marissa 22
## 3 34340 7 94 94.01780 1/7/94 Elizabeth Elizabeth 31
## 4 34343 10 94 94.02601 1/10/94 Zarina Zarina 35
## 5 34344 11 94 94.02875 1/11/94 Zarina Zarina 41
## 6 36210 50 99 99.13758 2/19/99 Kristen Kristen 78
## TypeofInteraction FemaleAgeclass FemaleMotherClass OffspringClass
## 1 Mating 1 Adult Female Mother Over 4 yrs
## 2 Mating 1 Adult Female Mother Over 4 yrs
## 3 Mating 1 Adult Female Mother Inf no juv
## 4 Mating 1 Adult Female Mother Over 4 yrs
## 5 Mating 1 Adult Female Mother Over 4 yrs
## 6 Mating 1 Adult Female Mother Inf ind juv
## ObservedReproductiveStatus EndocrineStatusHormones CyclePhaseHormones
## 1 L7
## 2 L8
## 3 L4
## 4
## 5
## 6 L1
## ReproductiveStatus Endo2 Endo3 Endo4 Endo5 Endo6 Endo7
## 1 Cycling non-POP non-POP non-POP non-POP non-POP non-POP
## 2 Cycling non-POP non-POP non-POP non-POP non-POP non-POP
## 3 Cycling non-POP Cycling POP POP Near-POP POP
## 4 Cycling non-POP non-POP non-POP non-POP non-POP non-POP
## 5 Cycling non-POP non-POP non-POP non-POP non-POP non-POP
## 6 Lactating non-cycling non-POP non-POP non-POP Lactating No Data
## Endo8 Pregnantvs.nonpregnant E1CmgCr PDGmgCr CommentMET
## 1 non-POP Non-Preg NA NA NA
## 2 non-POP Non-Preg NA NA NA
## 3 non-POP Non-Preg NA NA NA
## 4 non-POP Non-Preg NA NA NA
## 5 non-POP Non-Preg NA NA NA
## 6 No Data Non-Preg NA NA NA
## EndorineComment
## 1 ovulation occurs 15-17 Oct -- see attached
## 2 mid-luteal phase
## 3 High P 6 Jan-16 Jan, no data when E peak occurred, looks just after ovulation
## 4 Ovulation occurs between 1/16 and 1/22
## 5 Ovulation occurs between 1/16 and 1/22
## 6
## SocialInteraction InteractionwithMale Mating NearMating Consortship
## 1 Y Y Y N N
## 2 Y Y Y N N
## 3 Y Y Y N Y
## 4 Y Y Y N Y
## 5 Y Y Y N Y
## 6 Y Y Y N N
## ObservationMinutes FollowType FollowTypeII Lengthofmating
## 1 281 D found then full day D 0.006250
## 2 700 F full F 0.006053
## 3 785 L late full day F 0.001389
## 4 815 F full F 0.001273
## 5 820 F full F 0.003299
## 6 253 D found then full day 0.002894
## PelvicThrusts Male MaletypeI MaleTypeII MaleTypeIII
## 1 NA UML02Oct96 Unflanged Unflanged Non-Prime
## 2 NA UML03Apr97 Unflanged Unflanged Non-Prime
## 3 10 Gagung Unflanged Unflanged Non-Prime
## 4 NA Toby Unflanged Unflanged Non-Prime
## 5 98 Toby Unflanged Unflanged Non-Prime
## 6 NA Roman Flanged Past Prime Non-Prime
## MatingTypeExcel MatingTypePercent MatingTypePercentII MatingTypeII
## 1 Forced Forced Forced Forced
## 2 Forced Forced Forced Forced
## 3 Cooperative Cooperative Cooperative Unforced
## 4 Cooperative Cooperative Cooperative Unforced
## 5 Forced Forced Forced Forced
## 6 Forced Forced Forced Unforced
## MatingTypeIII Matingtypequestionable
## 1 Resisted No
## 2 Resisted No
## 3 Unresisted No
## 4 Unresisted No
## 5 Resisted No
## 6 Unresisted Yes
## TotalnonresistantProceptiveandResistanceBehaviors
## 1 2
## 2 4
## 3 3
## 4 1
## 5 2
## 6 5
## NonResistantBeforeBehaviors NonResistantDuringBehaviors
## 1 NA NA
## 2 NA 1
## 3 1 NA
## 4 1 NA
## 5 NA NA
## 6 NA 1
## Totalnonresistantbehaviors NonresistantBehaviors ProceptiveBehaviors
## 1 NA 0.00 NA
## 2 1 0.25 NA
## 3 1 0.33 1
## 4 1 1.00 NA
## 5 NA 0.00 NA
## 6 1 0.20 NA
## ProceptiveBehaviors_A TotalunresistedandProcepetivebehaviors
## 1 0.00 NA
## 2 0.00 1
## 3 0.33 2
## 4 0.00 1
## 5 0.00 NA
## 6 0.00 1
## unresistedandProcepetivebehaviors ResistantBeforeBehaviors
## 1 0.00 NA
## 2 0.25 1
## 3 0.67 1
## 4 1.00 NA
## 5 0.00 1
## 6 0.20 4
## BeforeBehaviors ResistantDuringBehaviors DuringBehaviors
## 1 0.00 2 1.0
## 2 0.25 2 0.5
## 3 0.33 NA 0.0
## 4 0.00 NA 0.0
## 5 0.50 1 0.5
## 6 0.80 NA 0.0
## TotalResistantBehaviors ResistantBehaviors AttractionBehaviors Mating01
## 1 2 1.00 1 1
## 2 3 0.75 NA 1
## 3 1 0.33 3 1
## 4 NA 0.00 NA 1
## 5 2 1.00 1 1
## 6 4 0.80 1 1
## POP preg lact prime past unflanged included endomiss
## 1 0 0 0 0 0 1 1 0
## 2 0 0 0 0 0 1 1 0
## 3 0 0 0 0 0 1 1 0
## 4 0 0 0 0 0 1 1 0
## 5 0 0 0 0 0 1 1 0
## 6 0 0 1 0 1 0 1 0totalmatings1 <- length(attributes(nonPOP_nonprime)$row.names)nonPOP_prime <- filter(nonPOP, prime == "1")
nonPOP_prime## [1] Date
## [2] day
## [3] year
## [4] year2
## [5] DateSPSS
## [6] Name
## [7] Name2
## [8] Record.
## [9] TypeofInteraction
## [10] FemaleAgeclass
## [11] FemaleMotherClass
## [12] OffspringClass
## [13] ObservedReproductiveStatus
## [14] EndocrineStatusHormones
## [15] CyclePhaseHormones
## [16] ReproductiveStatus
## [17] Endo2
## [18] Endo3
## [19] Endo4
## [20] Endo5
## [21] Endo6
## [22] Endo7
## [23] Endo8
## [24] Pregnantvs.nonpregnant
## [25] E1CmgCr
## [26] PDGmgCr
## [27] CommentMET
## [28] EndorineComment
## [29] SocialInteraction
## [30] InteractionwithMale
## [31] Mating
## [32] NearMating
## [33] Consortship
## [34] ObservationMinutes
## [35] FollowType
## [36] FollowTypeII
## [37] Lengthofmating
## [38] PelvicThrusts
## [39] Male
## [40] MaletypeI
## [41] MaleTypeII
## [42] MaleTypeIII
## [43] MatingTypeExcel
## [44] MatingTypePercent
## [45] MatingTypePercentII
## [46] MatingTypeII
## [47] MatingTypeIII
## [48] Matingtypequestionable
## [49] TotalnonresistantProceptiveandResistanceBehaviors
## [50] NonResistantBeforeBehaviors
## [51] NonResistantDuringBehaviors
## [52] Totalnonresistantbehaviors
## [53] NonresistantBehaviors
## [54] ProceptiveBehaviors
## [55] ProceptiveBehaviors_A
## [56] TotalunresistedandProcepetivebehaviors
## [57] unresistedandProcepetivebehaviors
## [58] ResistantBeforeBehaviors
## [59] BeforeBehaviors
## [60] ResistantDuringBehaviors
## [61] DuringBehaviors
## [62] TotalResistantBehaviors
## [63] ResistantBehaviors
## [64] AttractionBehaviors
## [65] Mating01
## [66] POP
## [67] preg
## [68] lact
## [69] prime
## [70] past
## [71] unflanged
## [72] included
## [73] endomiss
## <0 rows> (or 0-length row.names)totalmatings2 <- length(attributes(nonPOP_prime)$row.names)pregnant_nonprime <- filter(pregnant, prime == "0")
pregnant_nonprime## Date day year year2 DateSPSS Name Name2 Record.
## 1 33193 321 90 90.87748 11/17/90 Kristen Kristen 7
## 2 33194 322 90 90.88022 11/18/90 Kristen Kristen 8
## 3 33194 322 90 90.88022 11/18/90 Kristen Kristen 9
## 4 33248 10 91 91.02806 1/10/91 Female A Afemale 10
## 5 33257 19 91 91.05270 1/19/91 Beth Beth 11
## TypeofInteraction FemaleAgeclass FemaleMotherClass OffspringClass
## 1 Mating 1 Adult Female Mother Over 4 yrs
## 2 Mating 1 Adult Female Mother Over 4 yrs
## 3 Mating 2 Adult Female Mother Over 4 yrs
## 4 Mating 1 Adult Female Non-mother No infant
## 5 Mating 1 Adult Female Non-mother No infant
## ObservedReproductiveStatus EndocrineStatusHormones CyclePhaseHormones
## 1 Pregnant
## 2 Pregnant
## 3 Pregnant
## 4 Pregnant
## 5 Pregnant
## ReproductiveStatus Endo2 Endo3 Endo4 Endo5 Endo6
## 1 Pregnant non-cycling Pregnant Pregnant Pregnant Pregnant
## 2 Pregnant non-cycling Pregnant Pregnant Pregnant Pregnant
## 3 Pregnant non-cycling Pregnant Pregnant Pregnant Pregnant
## 4 Pregnant non-cycling Pregnant Pregnant Pregnant Pregnant
## 5 Pregnant non-cycling Pregnant Pregnant Pregnant Pregnant
## Endo7 Endo8 Pregnantvs.nonpregnant E1CmgCr PDGmgCr CommentMET
## 1 non-POP Pregnant Pregnant NA NA NA
## 2 non-POP Pregnant Pregnant NA NA NA
## 3 non-POP Pregnant Pregnant NA NA NA
## 4 non-POP Pregnant Pregnant NA NA NA
## 5 non-POP Pregnant Pregnant NA NA NA
## EndorineComment
## 1 Pregnant as of 22 Nov 94, no sample this date: could be pregnant already or conceptive cycle
## 2 Pregnant as of 22 Nov 94, no sample this date: could be pregnant already or conceptive cycle
## 3 Pregnant as of 22 Nov 94, no sample this date: could be pregnant already or conceptive cycle
## 4 Pregnant based on CK\032s analysis of E1C
## 5 High E & P
## SocialInteraction InteractionwithMale Mating NearMating Consortship
## 1 Y Y Y N Y
## 2 Y Y Y N Y
## 3 Y Y Y N Y
## 4 Y Y Y N Y
## 5 Y Y Y Y
## ObservationMinutes FollowType FollowTypeII Lengthofmating PelvicThrusts
## 1 NA N No data 0.009028 NA
## 2 424 H lost M 0.004861 NA
## 3 424 H lost M 0.021528 NA
## 4 617 H lost H 0.004861 NA
## 5 743 F full F 0.015972 NA
## Male MaletypeI MaleTypeII MaleTypeIII MatingTypeExcel
## 1 Male V Unflanged Unflanged Non-Prime Cooperative
## 2 Male V Unflanged Unflanged Non-Prime Cooperative
## 3 Male V Unflanged Unflanged Non-Prime Cooperative
## 4 Subadult T Unflanged Unflanged Non-Prime Cooperative
## 5 Phil Unflanged Unflanged Non-Prime Cooperative
## MatingTypePercent MatingTypePercentII MatingTypeII MatingTypeIII
## 1 Cooperative Cooperative Unforced Proceptive
## 2 No Data Cooperative Unforced Unresisted
## 3 Cooperative Cooperative Unforced Proceptive
## 4 Cooperative Cooperative Unforced Unresisted
## 5 Cooperative Cooperative Unforced Proceptive
## Matingtypequestionable TotalnonresistantProceptiveandResistanceBehaviors
## 1 No 2
## 2 No NA
## 3 No 2
## 4 No 1
## 5 No 4
## NonResistantBeforeBehaviors NonResistantDuringBehaviors
## 1 1 NA
## 2 NA NA
## 3 1 NA
## 4 1 NA
## 5 NA NA
## Totalnonresistantbehaviors NonresistantBehaviors ProceptiveBehaviors
## 1 1 0.5 1
## 2 NA NA NA
## 3 1 0.5 1
## 4 1 1.0 NA
## 5 NA 0.0 4
## ProceptiveBehaviors_A TotalunresistedandProcepetivebehaviors
## 1 0.5 2
## 2 NA NA
## 3 0.5 2
## 4 0.0 1
## 5 1.0 4
## unresistedandProcepetivebehaviors ResistantBeforeBehaviors
## 1 1 NA
## 2 NA NA
## 3 1 NA
## 4 1 NA
## 5 1 NA
## BeforeBehaviors ResistantDuringBehaviors DuringBehaviors
## 1 0 NA 0
## 2 NA NA NA
## 3 0 NA 0
## 4 0 NA 0
## 5 0 NA 0
## TotalResistantBehaviors ResistantBehaviors AttractionBehaviors Mating01
## 1 NA 0 1 1
## 2 NA NA NA 1
## 3 NA 0 NA 1
## 4 NA 0 1 1
## 5 NA 0 NA 1
## POP preg lact prime past unflanged included endomiss
## 1 0 1 0 0 0 1 1 0
## 2 0 1 0 0 0 1 1 0
## 3 0 1 0 0 0 1 1 0
## 4 0 1 0 0 0 1 1 0
## 5 0 1 0 0 0 1 1 0totalmatings3 <- length(attributes(pregnant_nonprime)$row.names)pregnant_prime <- filter(pregnant, prime == "1")
pregnant_prime## Date day year year2 DateSPSS Name Name2 Record. TypeofInteraction
## 1 35764 335 97 97.91650 12/1/97 Kayla Kayla 73 Mating 1
## 2 35765 336 97 97.91923 12/2/97 Kayla Kayla 75 Mating 1
## 3 35766 337 97 97.92197 12/3/97 Kayla Kayla 76 Mating 1
## FemaleAgeclass FemaleMotherClass OffspringClass
## 1 Adult Female Non-mother No infant
## 2 Adult Female Non-mother No infant
## 3 Adult Female Non-mother No infant
## ObservedReproductiveStatus EndocrineStatusHormones CyclePhaseHormones
## 1 Pregnant
## 2 Pregnant
## 3 Pregnant cycling Not sure
## ReproductiveStatus Endo2 Endo3 Endo4 Endo5 Endo6
## 1 Pregnant non-cycling Pregnant Pregnant Pregnant Pregnant
## 2 Pregnant non-cycling Pregnant Pregnant Pregnant Pregnant
## 3 Pregnant non-cycling Pregnant Pregnant Pregnant Pregnant
## Endo7 Endo8 Pregnantvs.nonpregnant E1CmgCr PDGmgCr CommentMET
## 1 non-POP Pregnant Pregnant NA NA NA
## 2 non-POP Pregnant Pregnant NA NA NA
## 3 non-POP Pregnant Pregnant NA 2275 NA
## EndorineComment SocialInteraction InteractionwithMale Mating NearMating
## 1 Y Y Y Y
## 2 Y Y Y N
## 3 Y Y Y N
## Consortship ObservationMinutes FollowType FollowTypeII
## 1 Y 285 L late full day
## 2 Y 880 F full F
## 3 Y 815 F full F
## Lengthofmating PelvicThrusts Male MaletypeI MaleTypeII MaleTypeIII
## 1 0.006944 374 Wendell Flanged Prime Male Prime
## 2 0.008333 384 Wendell Flanged Prime Male Prime
## 3 0.002083 NA Wendell Flanged Prime Male Prime
## MatingTypeExcel MatingTypePercent MatingTypePercentII MatingTypeII
## 1 Cooperative Cooperative Cooperative Unforced
## 2 Cooperative Cooperative Cooperative Unforced
## 3 Cooperative Cooperative Unforced
## MatingTypeIII Matingtypequestionable
## 1 Proceptive No
## 2 Proceptive No
## 3 Proceptive No
## TotalnonresistantProceptiveandResistanceBehaviors
## 1 4
## 2 7
## 3 5
## NonResistantBeforeBehaviors NonResistantDuringBehaviors
## 1 NA NA
## 2 NA NA
## 3 NA NA
## Totalnonresistantbehaviors NonresistantBehaviors ProceptiveBehaviors
## 1 NA 0 4
## 2 NA 0 7
## 3 NA 0 5
## ProceptiveBehaviors_A TotalunresistedandProcepetivebehaviors
## 1 1 4
## 2 1 7
## 3 1 5
## unresistedandProcepetivebehaviors ResistantBeforeBehaviors
## 1 1 NA
## 2 1 NA
## 3 1 NA
## BeforeBehaviors ResistantDuringBehaviors DuringBehaviors
## 1 0 NA 0
## 2 0 NA 0
## 3 0 NA 0
## TotalResistantBehaviors ResistantBehaviors AttractionBehaviors Mating01
## 1 NA 0 2 1
## 2 NA 0 5 1
## 3 NA 0 NA 1
## POP preg lact prime past unflanged included endomiss
## 1 0 1 0 1 0 0 1 0
## 2 0 1 0 1 0 0 1 0
## 3 0 1 0 1 0 0 1 0totalmatings4 <- length(attributes(pregnant_prime)$row.names)POP_nonprime <- filter(POP, prime == "0")
POP_nonprime## Date day year year2 DateSPSS Name Name2 Record. TypeofInteraction
## 1 34347 14 94 94.03696 1/14/94 Zarina Zarina 42 Mating 1
## FemaleAgeclass FemaleMotherClass OffspringClass
## 1 Adult Female Mother Over 4 yrs
## ObservedReproductiveStatus EndocrineStatusHormones CyclePhaseHormones
## 1
## ReproductiveStatus Endo2 Endo3 Endo4 Endo5 Endo6 Endo7 Endo8
## 1 Cycling POP Cycling POP POP POP POP POP
## Pregnantvs.nonpregnant E1CmgCr PDGmgCr CommentMET
## 1 Non-Preg NA NA NA
## EndorineComment SocialInteraction
## 1 Ovulation occurs between 1/16 and 1/22 Y
## InteractionwithMale Mating NearMating Consortship ObservationMinutes
## 1 Y Y N Y 795
## FollowType FollowTypeII Lengthofmating PelvicThrusts Male MaletypeI
## 1 F full F 0.004086 261 Roman Flanged
## MaleTypeII MaleTypeIII MatingTypeExcel MatingTypePercent
## 1 Past Prime Non-Prime Forced Forced
## MatingTypePercentII MatingTypeII MatingTypeIII Matingtypequestionable
## 1 Forced Forced Resisted No
## TotalnonresistantProceptiveandResistanceBehaviors
## 1 2
## NonResistantBeforeBehaviors NonResistantDuringBehaviors
## 1 NA NA
## Totalnonresistantbehaviors NonresistantBehaviors ProceptiveBehaviors
## 1 NA 0 NA
## ProceptiveBehaviors_A TotalunresistedandProcepetivebehaviors
## 1 0 NA
## unresistedandProcepetivebehaviors ResistantBeforeBehaviors
## 1 0 NA
## BeforeBehaviors ResistantDuringBehaviors DuringBehaviors
## 1 0 2 1
## TotalResistantBehaviors ResistantBehaviors AttractionBehaviors Mating01
## 1 2 1 2 1
## POP preg lact prime past unflanged included endomiss
## 1 1 0 0 0 1 0 1 0totalmatings5 <- length(attributes(POP_nonprime)$row.names)POP_prime <- filter(POP, prime == "1")
POP_prime## Date day year year2 DateSPSS Name Name2 Record.
## 1 34318 350 93 93.95756 12/16/93 Marissa Marissa 26
## 2 34318 350 93 93.95756 12/16/93 Marissa Marissa 27
## 3 34340 7 94 94.01780 1/7/94 Marissa Marissa 30
## 4 34341 8 94 94.02053 1/8/94 Marissa Marissa 32
## 5 34343 10 94 94.02601 1/10/94 Marissa Marissa 39
## 6 34349 16 94 94.04244 1/16/94 Zarina Zarina 45
## TypeofInteraction FemaleAgeclass FemaleMotherClass OffspringClass
## 1 Mating 1 Adult Female Mother Over 4 yrs
## 2 Mating 2 Adult Female Mother Over 4 yrs
## 3 Mating 1 Adult Female Mother Over 4 yrs
## 4 Mating 1 Adult Female Mother Over 4 yrs
## 5 Mating 1 Adult Female Mother Over 4 yrs
## 6 Mating 1 Adult Female Mother Over 4 yrs
## ObservedReproductiveStatus EndocrineStatusHormones CyclePhaseHormones
## 1 L8 cycling pop
## 2 L8 cycling pop
## 3
## 4
## 5 cycling near-pop
## 6
## ReproductiveStatus Endo2 Endo3 Endo4 Endo5 Endo6 Endo7
## 1 Cycling POP Cycling POP POP POP POP
## 2 Cycling POP Cycling POP POP POP POP
## 3 Cycling Near-POP Cycling POP non-POP Near-POP non-POP
## 4 Cycling Near-POP Cycling POP non-POP Near-POP non-POP
## 5 Cycling Near-POP Cycling POP non-POP Near-POP non-POP
## 6 Cycling POP Cycling POP POP POP POP
## Endo8 Pregnantvs.nonpregnant E1CmgCr PDGmgCr CommentMET
## 1 POP Non-Preg 46754 1249 NA
## 2 POP Non-Preg 46754 1249 NA
## 3 non-POP Non-Preg NA NA NA
## 4 non-POP Non-Preg NA NA NA
## 5 non-POP Non-Preg 65153 891 NA
## 6 POP Non-Preg NA NA NA
## EndorineComment SocialInteraction
## 1 ovulation occurs approx 15-16 Dec see attached Y
## 2 ovulation occurs approx 15-16 Dec see attached Y
## 3 ovulation should occur approx 15-17 Jan see attached Y
## 4 ovulation should occur approx 15-17 Jan see attached Y
## 5 ovulation should occur approx 15-17 Jan see attached Y
## 6 Ovulation occurs between 1/16 and 1/22 Y
## InteractionwithMale Mating NearMating Consortship ObservationMinutes
## 1 Y Y Y Y 667
## 2 Y Y Y Y 667
## 3 Y Y Y N 806
## 4 Y Y Y Y 792
## 5 Y Y Y Y 770
## 6 Y Y Y Y 648
## FollowType FollowTypeII Lengthofmating PelvicThrusts Male
## 1 F full F 0.003785 NA Jari Manis
## 2 F full F 0.004074 NA Jari Manis
## 3 F full F 0.002743 NA Jari Manis
## 4 F full F 0.010266 NA Jari Manis
## 5 F full F 0.001910 224 Jari Manis
## 6 H lost 0.009028 113 Jari Manis
## MaletypeI MaleTypeII MaleTypeIII MatingTypeExcel MatingTypePercent
## 1 Flanged Prime Male Prime Cooperative Cooperative
## 2 Flanged Prime Male Prime Cooperative Cooperative
## 3 Flanged Prime Male Prime Forced Forced
## 4 Flanged Prime Male Prime Forced Cooperative
## 5 Flanged Prime Male Prime Forced Forced
## 6 Flanged Prime Male Prime Cooperative Cooperative
## MatingTypePercentII MatingTypeII MatingTypeIII Matingtypequestionable
## 1 Cooperative Unforced Unresisted No
## 2 Cooperative Unforced Unresisted No
## 3 Forced Forced Resisted No
## 4 Cooperative Unforced Unresisted Yes
## 5 Forced Forced Resisted No
## 6 Cooperative Unforced Proceptive No
## TotalnonresistantProceptiveandResistanceBehaviors
## 1 1
## 2 2
## 3 3
## 4 3
## 5 3
## 6 6
## NonResistantBeforeBehaviors NonResistantDuringBehaviors
## 1 1 NA
## 2 NA 1
## 3 NA 1
## 4 1 1
## 5 NA 1
## 6 NA NA
## Totalnonresistantbehaviors NonresistantBehaviors ProceptiveBehaviors
## 1 1 1.00 NA
## 2 1 0.50 NA
## 3 1 0.33 NA
## 4 2 0.67 NA
## 5 1 0.33 NA
## 6 NA 0.00 6
## ProceptiveBehaviors_A TotalunresistedandProcepetivebehaviors
## 1 0 1
## 2 0 1
## 3 0 1
## 4 0 2
## 5 0 1
## 6 1 6
## unresistedandProcepetivebehaviors ResistantBeforeBehaviors
## 1 1.00 NA
## 2 0.50 1
## 3 0.33 1
## 4 0.67 1
## 5 0.33 1
## 6 1.00 NA
## BeforeBehaviors ResistantDuringBehaviors DuringBehaviors
## 1 0.00 NA 0.00
## 2 0.50 NA 0.00
## 3 0.33 1 0.33
## 4 0.33 NA 0.00
## 5 0.33 1 0.33
## 6 0.00 NA 0.00
## TotalResistantBehaviors ResistantBehaviors AttractionBehaviors Mating01
## 1 NA 0.00 2 1
## 2 1 0.50 3 1
## 3 2 0.67 2 1
## 4 1 0.33 1 1
## 5 2 0.67 3 1
## 6 NA 0.00 NA 1
## POP preg lact prime past unflanged included endomiss
## 1 1 0 0 1 0 0 1 0
## 2 1 0 0 1 0 0 1 0
## 3 1 0 0 1 0 0 1 0
## 4 1 0 0 1 0 0 1 0
## 5 1 0 0 1 0 0 1 0
## 6 1 0 0 1 0 0 1 0totalmatings6 <- length(attributes(POP_prime)$row.names)Finally, I created a vector of each of the values of occurences, and constructed a bar plot from this vector.
v <- c(totalmatings1, totalmatings2, totalmatings3, totalmatings4, totalmatings5, totalmatings6)
barplot(v, main = "Figure 1", xlab = "female endocrine status", ylab = "mating events observed", names.arg = c("non-periovulatory","non-periovulatory","pregnant","pregnant", "periovulatory", "periovulatory"), col = "black", density = c(100, 0, 100, 0, 100, 0), space = c(1, 0, 1, 0, 1, 0))
# this extra code is just so it has the same colors and labels as the original figureThis is what the original figure from the paper looked like:
They are the same! As stated in the paper, this figure shows that non-periovulatory females mated most frequently with non-prime males, while periovulatory females mated most frequently with prime males.
It is possible that there is a more concise way to replicate this figure, though this way seemed the most logical to me, given the format of the data.
Figure 2 (Descriptive)
The second figure in the paper shows the distribution of female encounters with males, with respect to male type and female ovulatory status. I planned to use a similar method to create this figure as I did for the first one (the dependent variable is now ‘encounters observed’), though I ran into a lot of problems.
I recieved this dataset from my professor who is first author on this paper. She did not have the original files, so she reached out to the author who ran all the statistics for the paper. He emailed her zipped folders of all of the old versions of the dataset that he had. She then forwarded these folders to me, which contained hundreds of different documents which spanned the course of nearly 3 years. Many of these files were different updated versions of each other. Unfortunately, all the observation data was spread out between different files which led to confusion. Additionally, the same writing conventions were not used throughout all the data. For example, some variables were recorded as full words like “Prime” and “Nonprime”, while others were recorded as binary numbers “0” and “1”, and others simply said “Y” or “N”.
Attempt 1
To begin the second replication, I first converted another SPSS file into csv format and loaded it in to R. This file contains all 2460 orangutan follows.
# getting the data into R
library(curl)
f2 <- curl("https://raw.githubusercontent.com/natalierobinson96/data-reanalysis-assignment/master/interactions.csv")
d2 <- read.csv(f2, header = TRUE, sep = ",")
head(d2)## Date day year year2 DateSPSS Name Name2 Record.
## 1 34397 64 94 94.17385 03/05/1994 Marissa Marissa 959
## 2 34398 65 94 94.17659 03/06/1994 Marissa Marissa 961
## 3 34504 171 94 94.46680 06/20/1994 Abby Abby 1107
## 4 34505 172 94 94.46954 06/21/1994 Abby Abby 1110
## 5 34506 173 94 94.47228 06/22/1994 Abby Abby 1113
## 6 34507 174 94 94.47502 06/23/1994 Abby Abby 1116
## TypeofInteraction FemaleAgeclass FemaleMotherClass OffspringClass
## 1 Adult Female Mother Over 4 yrs
## 2 Adult Female Mother Over 4 yrs
## 3 Adult Female Mother Over 4 yrs
## 4 Adult Female Mother Over 4 yrs
## 5 Adult Female Mother Over 4 yrs
## 6 Adult Female Mother Over 4 yrs
## ObservedReproductiveStatus EndocrineStatusHormones CyclePhaseHormones
## 1 Pregnant
## 2 Pregnant
## 3 Unknown
## 4 Unknown cycling non-pop
## 5 Unknown cycling non-pop
## 6 Unknown cycling non-pop
## ReproductiveStatus Endo2 Endo3 Endo4 Endo5 Endo6
## 1 Conception or pregnant non-cycling Pregnant Pregnant Pregnant Pregnant
## 2 Conception or pregnant non-cycling Pregnant Pregnant Pregnant Pregnant
## 3 Cycling Cycling Cycling Cycling Cycling No Data
## 4 Cycling non-POP non-POP non-POP non-POP non-POP
## 5 Cycling non-POP non-POP non-POP non-POP non-POP
## 6 Cycling non-POP non-POP non-POP non-POP non-POP
## Endo7 Endo8 Pregnantvs.nonpregnant E1CmgCr PDGmgCr CommentMET
## 1 non-POP Pregnant Pregnant NA NA
## 2 non-POP Pregnant Pregnant NA NA
## 3 No Data No Data Non-Preg NA NA
## 4 non-POP non-POP Non-Preg 4695 1776
## 5 non-POP non-POP Non-Preg 8165 1768
## 6 non-POP non-POP Non-Preg 6824 1938
## EndorineComment SocialInteraction InteractionwithMale Mating NearMating
## 1 Y N N N
## 2 N N N N
## 3 N N N N
## 4 Y N N N
## 5 N N N N
## 6 N N N N
## Consortship ObservationMinutes FollowType FollowTypeII
## 1 N 555 D found then full day
## 2 N 300 H lost
## 3 N 392 D found then full day
## 4 N 799 F full F
## 5 N 692 F full F
## 6 N 780 F full F
## Lengthofmating PelvicThrusts Male MaletypeI MaleTypeII MaleTypeIII
## 1 NA NA
## 2 NA NA
## 3 NA NA
## 4 NA NA
## 5 NA NA
## 6 NA NA
## MatingTypeExcel MatingTypePercent MatingTypePercentII MatingTypeII
## 1
## 2
## 3
## 4
## 5
## 6
## MatingTypeIII Matingtypequestionable
## 1
## 2
## 3
## 4
## 5
## 6
## TotalnonresistantProceptiveandResistanceBehaviors
## 1 NA
## 2 NA
## 3 NA
## 4 NA
## 5 NA
## 6 NA
## NonResistantBeforeBehaviors NonResistantDuringBehaviors
## 1 NA NA
## 2 NA NA
## 3 NA NA
## 4 NA NA
## 5 NA NA
## 6 NA NA
## Totalnonresistantbehaviors NonresistantBehaviors ProceptiveBehaviors
## 1 NA NA NA
## 2 NA NA NA
## 3 NA NA NA
## 4 NA NA NA
## 5 NA NA NA
## 6 NA NA NA
## ProceptiveBehaviors_A TotalunresistedandProcepetivebehaviors
## 1 NA NA
## 2 NA NA
## 3 NA NA
## 4 NA NA
## 5 NA NA
## 6 NA NA
## unresistedandProcepetivebehaviors ResistantBeforeBehaviors
## 1 NA NA
## 2 NA NA
## 3 NA NA
## 4 NA NA
## 5 NA NA
## 6 NA NA
## BeforeBehaviors ResistantDuringBehaviors DuringBehaviors
## 1 NA NA NA
## 2 NA NA NA
## 3 NA NA NA
## 4 NA NA NA
## 5 NA NA NA
## 6 NA NA NA
## TotalResistantBehaviors ResistantBehaviors AttractionBehaviors Mating01
## 1 NA NA NA 0
## 2 NA NA NA 0
## 3 NA NA NA 0
## 4 NA NA NA 0
## 5 NA NA NA 0
## 6 NA NA NA 0
## POP preg lact prime past unflanged included endomiss
## 1 0 0 0 NA NA NA 0 0
## 2 0 0 0 NA NA NA 0 0
## 3 0 0 0 NA NA NA 0 1
## 4 0 0 0 NA NA NA 0 0
## 5 0 0 0 NA NA NA 0 0
## 6 0 0 0 NA NA NA 0 0Next I created a new dataframe which only includes the observation entries that were included in the final analysis. These are the observations in which a male and female interacted with each other, and both male and female reproductive status were known.
d2 <- filter(d2, InteractionwithMale == "Y")
d2 <- filter(d2, included == "1")
head(d2)## Date day year year2 DateSPSS Name Name2 Record.
## 1 34343 10 94 94.02601 01/10/1994 AF10Jan98\v AF10Jan98\v 36
## 2 34343 10 94 94.02601 01/10/1994 AF10Jan98\v AF10Jan98\v 37
## 3 33168 296 90 90.80903 10/23/1990 Betina A Betina 100
## 4 34338 5 94 94.01232 01/05/1994 Betina Q Betina 867
## 5 34340 7 94 94.01780 01/07/1994 Betina P Betina 875
## 6 34341 8 94 94.02053 01/08/1994 Betina P Betina 877
## TypeofInteraction FemaleAgeclass FemaleMotherClass OffspringClass
## 1 Mating 1 Adult Female Non-mother No infant
## 2 Mating 2 Adult Female Non-mother No infant
## 3 Adult Female Non-mother No infant
## 4 Consortship Adult Female Mother Over 4 yrs
## 5 Consortship Adult Female Mother Over 4 yrs
## 6 Adult Female Mother Over 4 yrs
## ObservedReproductiveStatus EndocrineStatusHormones CyclePhaseHormones
## 1
## 2
## 3 Cycling
## 4
## 5 cycling Not sure
## 6
## ReproductiveStatus Endo2 Endo3 Endo4 Endo5 Endo6 Endo7
## 1 Cycling Cycling Cycling Cycling Cycling No Data No Data
## 2 Cycling Cycling Cycling Cycling Cycling No Data No Data
## 3 Cycling Cycling Cycling Cycling Cycling No Data No Data
## 4 Cycling Cycling Cycling Cycling Cycling No Data No Data
## 5 Cycling Cycling Cycling Cycling Cycling No Data No Data
## 6 Cycling Cycling Cycling Cycling Cycling No Data No Data
## Endo8 Pregnantvs.nonpregnant E1CmgCr PDGmgCr CommentMET
## 1 No Data Non-Preg NA NA
## 2 No Data Non-Preg NA NA
## 3 No Data Non-Preg NA NA
## 4 No Data Non-Preg NA NA
## 5 No Data Non-Preg 19850 647
## 6 No Data Non-Preg NA NA
## EndorineComment SocialInteraction InteractionwithMale Mating NearMating
## 1 Y Y Y
## 2 Y Y Y
## 3 Y Y
## 4 Y Y N N
## 5 Y Y N N
## 6 Y Y N N
## Consortship ObservationMinutes FollowType FollowTypeII
## 1 NA N No data
## 2 NA N No data
## 3 285 D found then full day D
## 4 Y 465 T late and lost
## 5 Y 815 F full F
## 6 N 338 T late and lost
## Lengthofmating PelvicThrusts Male MaletypeI MaleTypeII
## 1 0.002083 NA Jari Manis Flanged Prime Male
## 2 NA NA Jari Manis Flanged Prime Male
## 3 NA NA Rob Unflanged Unflanged
## 4 NA NA Roman Flanged Past Prime
## 5 0.379167 NA Unflanged Male Unflanged Unflanged
## 6 NA NA Gagung Unflanged Unflanged
## MaleTypeIII MatingTypeExcel MatingTypePercent MatingTypePercentII
## 1 Prime Forced Forced Forced
## 2 Prime Cooperative Cooperative Cooperative
## 3 Non-Prime
## 4 Non-Prime
## 5 Non-Prime
## 6 Non-Prime
## MatingTypeII MatingTypeIII Matingtypequestionable
## 1 Forced Resisted No
## 2 Unforced Unresisted No
## 3
## 4
## 5
## 6
## TotalnonresistantProceptiveandResistanceBehaviors
## 1 3
## 2 2
## 3 NA
## 4 NA
## 5 NA
## 6 NA
## NonResistantBeforeBehaviors NonResistantDuringBehaviors
## 1 NA NA
## 2 1 1
## 3 NA NA
## 4 NA NA
## 5 NA NA
## 6 NA NA
## Totalnonresistantbehaviors NonresistantBehaviors ProceptiveBehaviors
## 1 NA 0 NA
## 2 2 1 NA
## 3 NA NA NA
## 4 NA NA NA
## 5 NA NA NA
## 6 NA NA NA
## ProceptiveBehaviors_A TotalunresistedandProcepetivebehaviors
## 1 0 NA
## 2 0 2
## 3 NA NA
## 4 NA NA
## 5 NA NA
## 6 NA NA
## unresistedandProcepetivebehaviors ResistantBeforeBehaviors
## 1 0 1
## 2 1 NA
## 3 NA NA
## 4 NA NA
## 5 NA NA
## 6 NA NA
## BeforeBehaviors ResistantDuringBehaviors DuringBehaviors
## 1 0.33 2 0.67
## 2 0.00 NA 0.00
## 3 NA NA NA
## 4 NA NA NA
## 5 NA NA NA
## 6 NA NA NA
## TotalResistantBehaviors ResistantBehaviors AttractionBehaviors Mating01
## 1 3 1 1 1
## 2 NA 0 1 1
## 3 NA NA NA 0
## 4 NA NA NA 0
## 5 NA NA NA 0
## 6 NA NA NA 0
## POP preg lact prime past unflanged included endomiss
## 1 0 0 0 1 0 0 1 1
## 2 0 0 0 1 0 0 1 1
## 3 0 0 0 0 0 1 1 1
## 4 0 0 0 0 1 0 1 1
## 5 0 0 0 0 0 1 1 1
## 6 0 0 0 0 0 1 1 1This contains 331 observations, however, the paper states that only 153 encounters were included in the analysis. I continued on anyway.
As before, I created the 3 categories of female endocrine status:
nonPOP <- filter(d2, lact == "1")
head(nonPOP) #matings with females not in periovulatory period; currently cycling## Date day year year2 DateSPSS Name Name2 Record.
## 1 33256 18 91 91.04997 01/18/1991 Anissa Anissa 173
## 2 33953 350 92 92.95825 12/15/1992 Beth Beth 542
## 3 34007 39 93 93.10609 02/08/1993 Betina J Betina 21
## 4 33167 295 90 90.80630 10/22/1990 Celia Celia 99
## 5 34131 163 93 93.44559 06/12/1993 Elizabeth Elizabeth 23
## 6 33234 362 90 90.98973 12/28/1990 Elizabeth Elizabeth 160
## TypeofInteraction FemaleAgeclass FemaleMotherClass OffspringClass
## 1 Adult Female Mother Inf no juv
## 2 Adult Female Mother Inf no juv
## 3 Attempt 1 Adult Female Mother Inf no juv
## 4 Encounter Adult Female Mother Inf no juv
## 5 Attempt 1 Adult Female Mother Inf no juv
## 6 Consortship Adult Female Mother Inf ind juv
## ObservedReproductiveStatus EndocrineStatusHormones CyclePhaseHormones
## 1 L1
## 2 L2
## 3
## 4 L2
## 5 L4
## 6 L1 Lactating non-cycling
## ReproductiveStatus Endo2 Endo3 Endo4 Endo5 Endo6 Endo7
## 1 Lactating non-cycling non-POP non-POP non-POP Lactating No Data
## 2 Lactating non-cycling non-POP non-POP non-POP Lactating No Data
## 3 Lactating non-cycling non-POP non-POP non-POP Lactating No Data
## 4 Lactating non-cycling non-POP non-POP non-POP Lactating No Data
## 5 Lactating non-cycling non-POP non-POP non-POP Lactating No Data
## 6 Lactating non-cycling non-POP non-POP non-POP Lactating No Data
## Endo8 Pregnantvs.nonpregnant E1CmgCr PDGmgCr CommentMET
## 1 No Data Non-Preg NA NA
## 2 No Data Non-Preg NA NA
## 3 No Data Non-Preg NA NA
## 4 No Data Non-Preg NA NA
## 5 No Data Non-Preg NA NA
## 6 No Data Non-Preg 12929 804
## EndorineComment SocialInteraction InteractionwithMale Mating NearMating
## 1 Y Y
## 2 Y Y N N
## 3 Y Y N Y
## 4 Y Y
## 5 Y Y N Y
## 6 Y Y N N
## Consortship ObservationMinutes FollowType FollowTypeII
## 1 528 D found then full day D
## 2 N 733 L late full day F
## 3 Y 194 D found then full day
## 4 333 D found then full day M
## 5 Y 590 D found then full day
## 6 Y 725 F full F
## Lengthofmating PelvicThrusts Male MaletypeI MaleTypeII
## 1 NA NA Unflanged male Unflanged Unflanged
## 2 NA NA Unflanged Male Unflanged Unflanged
## 3 NA NA Unflanged Male Unflanged Unflanged
## 4 0.004861 NA Unflanged male Unflanged Unflanged
## 5 NA NA Roman Flanged Prime Male
## 6 NA NA Unflanged Male Unflanged Unflanged
## MaleTypeIII MatingTypeExcel MatingTypePercent MatingTypePercentII
## 1 Non-Prime
## 2 Non-Prime
## 3 Non-Prime
## 4 Non-Prime
## 5 Prime
## 6 Non-Prime
## MatingTypeII MatingTypeIII Matingtypequestionable
## 1
## 2
## 3
## 4
## 5
## 6
## TotalnonresistantProceptiveandResistanceBehaviors
## 1 NA
## 2 NA
## 3 NA
## 4 NA
## 5 NA
## 6 NA
## NonResistantBeforeBehaviors NonResistantDuringBehaviors
## 1 NA NA
## 2 NA NA
## 3 NA NA
## 4 NA NA
## 5 NA NA
## 6 NA NA
## Totalnonresistantbehaviors NonresistantBehaviors ProceptiveBehaviors
## 1 NA NA NA
## 2 NA NA NA
## 3 NA NA NA
## 4 NA NA NA
## 5 NA NA NA
## 6 NA NA NA
## ProceptiveBehaviors_A TotalunresistedandProcepetivebehaviors
## 1 NA NA
## 2 NA NA
## 3 NA NA
## 4 NA NA
## 5 NA NA
## 6 NA NA
## unresistedandProcepetivebehaviors ResistantBeforeBehaviors
## 1 NA NA
## 2 NA NA
## 3 NA NA
## 4 NA NA
## 5 NA NA
## 6 NA NA
## BeforeBehaviors ResistantDuringBehaviors DuringBehaviors
## 1 NA NA NA
## 2 NA NA NA
## 3 NA NA NA
## 4 NA NA NA
## 5 NA NA NA
## 6 NA NA NA
## TotalResistantBehaviors ResistantBehaviors AttractionBehaviors Mating01
## 1 NA NA NA 0
## 2 NA NA NA 0
## 3 NA NA NA 0
## 4 NA NA NA 0
## 5 NA NA NA 0
## 6 NA NA NA 0
## POP preg lact prime past unflanged included endomiss
## 1 0 0 1 0 0 1 1 0
## 2 0 0 1 0 0 1 1 0
## 3 0 0 1 0 0 1 1 0
## 4 0 0 1 0 0 1 1 0
## 5 0 0 1 1 0 0 1 0
## 6 0 0 1 0 0 1 1 0pregnant <- filter(d2, preg == "1")
head(pregnant) #matings with pregnant females ## Date day year year2 DateSPSS Name Name2 Record.
## 1 33248 10 91 91.02806 01/10/1991 Female A Afemale 10
## 2 33247 9 91 91.02533 01/09/1991 Female A Afemale 169
## 3 33257 19 91 91.05270 01/19/1991 Beth Beth 11
## 4 33261 23 91 91.06366 01/23/1991 Beth Beth 182
## 5 33292 54 91 91.14853 02/23/1991 Beth Beth 221
## 6 33293 55 91 91.15127 02/24/1991 Beth Beth 223
## TypeofInteraction FemaleAgeclass FemaleMotherClass OffspringClass
## 1 Mating 1 Adult Female Non-mother No infant
## 2 Consortship Adult Female Non-mother No infant
## 3 Mating 1 Adult Female Non-mother No infant
## 4 Consortship Adult Female Non-mother No infant
## 5 Consortship Adult Female Non-mother No infant
## 6 Consortship Adult Female Non-mother No infant
## ObservedReproductiveStatus EndocrineStatusHormones CyclePhaseHormones
## 1 Pregnant
## 2 Pregnant
## 3 Pregnant
## 4 Pregnant
## 5 Pregnant
## 6 Pregnant
## ReproductiveStatus Endo2 Endo3 Endo4 Endo5 Endo6
## 1 Pregnant non-cycling Pregnant Pregnant Pregnant Pregnant
## 2 Pregnant non-cycling Pregnant Pregnant Pregnant Pregnant
## 3 Pregnant non-cycling Pregnant Pregnant Pregnant Pregnant
## 4 Pregnant non-cycling Pregnant Pregnant Pregnant Pregnant
## 5 Pregnant non-cycling Pregnant Pregnant Pregnant Pregnant
## 6 Pregnant non-cycling Pregnant Pregnant Pregnant Pregnant
## Endo7 Endo8 Pregnantvs.nonpregnant E1CmgCr PDGmgCr CommentMET
## 1 non-POP Pregnant Pregnant NA NA
## 2 non-POP Pregnant Pregnant NA NA
## 3 non-POP Pregnant Pregnant NA NA
## 4 non-POP Pregnant Pregnant NA NA
## 5 non-POP Pregnant Pregnant NA NA
## 6 non-POP Pregnant Pregnant NA NA
## EndorineComment SocialInteraction
## 1 Pregnant based on CK\032s analysis of E1C Y
## 2 Y
## 3 High E & P Y
## 4 Y
## 5 Y
## 6 Y
## InteractionwithMale Mating NearMating Consortship ObservationMinutes
## 1 Y Y N Y 617
## 2 Y Y NA
## 3 Y Y Y 743
## 4 Y Y Y 538
## 5 Y N N Y 160
## 6 Y Y 705
## FollowType FollowTypeII Lengthofmating PelvicThrusts
## 1 H lost H 0.004861 NA
## 2 N No data NA NA
## 3 F full F 0.015972 NA
## 4 D found then full day D NA NA
## 5 D found then full day D NA NA
## 6 L late full day F NA NA
## Male MaletypeI MaleTypeII MaleTypeIII MatingTypeExcel
## 1 Subadult T Unflanged Unflanged Non-Prime Cooperative
## 2 Subadult T Unflanged Unflanged Non-Prime
## 3 Phil Unflanged Unflanged Non-Prime Cooperative
## 4 Phil Unflanged Unflanged Non-Prime
## 5 Male E Flanged Prime Male Prime
## 6 Male E Flanged Prime Male Prime
## MatingTypePercent MatingTypePercentII MatingTypeII MatingTypeIII
## 1 Cooperative Cooperative Unforced Unresisted
## 2
## 3 Cooperative Cooperative Unforced Proceptive
## 4
## 5
## 6
## Matingtypequestionable TotalnonresistantProceptiveandResistanceBehaviors
## 1 No 1
## 2 NA
## 3 No 4
## 4 NA
## 5 NA
## 6 NA
## NonResistantBeforeBehaviors NonResistantDuringBehaviors
## 1 1 NA
## 2 NA NA
## 3 NA NA
## 4 NA NA
## 5 NA NA
## 6 NA NA
## Totalnonresistantbehaviors NonresistantBehaviors ProceptiveBehaviors
## 1 1 1 NA
## 2 NA NA NA
## 3 NA 0 4
## 4 NA NA NA
## 5 NA NA NA
## 6 NA NA NA
## ProceptiveBehaviors_A TotalunresistedandProcepetivebehaviors
## 1 0 1
## 2 NA NA
## 3 1 4
## 4 NA NA
## 5 NA NA
## 6 NA NA
## unresistedandProcepetivebehaviors ResistantBeforeBehaviors
## 1 1 NA
## 2 NA NA
## 3 1 NA
## 4 NA NA
## 5 NA NA
## 6 NA NA
## BeforeBehaviors ResistantDuringBehaviors DuringBehaviors
## 1 0 NA 0
## 2 NA NA NA
## 3 0 NA 0
## 4 NA NA NA
## 5 NA NA NA
## 6 NA NA NA
## TotalResistantBehaviors ResistantBehaviors AttractionBehaviors Mating01
## 1 NA 0 1 1
## 2 NA NA NA 0
## 3 NA 0 NA 1
## 4 NA NA NA 0
## 5 NA NA NA 0
## 6 NA NA NA 0
## POP preg lact prime past unflanged included endomiss
## 1 0 1 0 0 0 1 1 0
## 2 0 1 0 0 0 1 1 0
## 3 0 1 0 0 0 1 1 0
## 4 0 1 0 0 0 1 1 0
## 5 0 1 0 1 0 0 1 0
## 6 0 1 0 1 0 0 1 0POP <- filter(d2, POP == "1")
head(POP) #matings with females in periovulatory period## Date day year year2 DateSPSS Name Name2 Record.
## 1 34297 329 93 93.90007 11/25/1993 Kristen Kristen 797
## 2 34299 331 93 93.90554 11/27/1993 Kristen Kristen 806
## 3 33502 264 91 91.72348 09/21/1991 Marissa Marissa 19
## 4 34318 350 93 93.95756 12/16/1993 Marissa Marissa 26
## 5 34318 350 93 93.95756 12/16/1993 Marissa Marissa 27
## 6 34340 7 94 94.01780 01/07/1994 Marissa Marissa 29
## TypeofInteraction FemaleAgeclass FemaleMotherClass OffspringClass
## 1 Adult Female Mother Inf ind juv
## 2 Adult Female Mother Inf ind juv
## 3 Consortship Adult Female Mother Over 4 yrs
## 4 Mating 1 Adult Female Mother Over 4 yrs
## 5 Mating 2 Adult Female Mother Over 4 yrs
## 6 Attempt 1 Adult Female Mother Over 4 yrs
## ObservedReproductiveStatus EndocrineStatusHormones CyclePhaseHormones
## 1 L3 cycling pop
## 2 L3 cycling pop
## 3 L6 cycling pop
## 4 L8 cycling pop
## 5 L8 cycling pop
## 6
## ReproductiveStatus Endo2 Endo3 Endo4 Endo5 Endo6 Endo7
## 1 Cycling POP Cycling POP POP POP POP
## 2 Cycling POP Cycling POP POP POP POP
## 3 Cycling POP Cycling POP POP POP POP
## 4 Cycling POP Cycling POP POP POP POP
## 5 Cycling POP Cycling POP POP POP POP
## 6 Cycling Near-POP Cycling POP non-POP Near-POP non-POP
## Endo8 Pregnantvs.nonpregnant E1CmgCr PDGmgCr
## 1 POP Non-Preg 30267 609
## 2 POP Non-Preg 9672 122
## 3 POP Non-Preg 21223 687
## 4 POP Non-Preg 46754 1249
## 5 POP Non-Preg 46754 1249
## 6 non-POP Non-Preg NA NA
## CommentMET
## 1
## 2 following days are def. luteal, but can't tell when E peak occurred
## 3
## 4
## 5
## 6
## EndorineComment
## 1
## 2
## 3 Called POP because PDG peaks two days later; but not really sure it is POP
## 4 ovulation occurs approx 15-16 Dec see attached
## 5 ovulation occurs approx 15-16 Dec see attached
## 6 ovulation should occur approx 15-17 Jan see attached
## SocialInteraction InteractionwithMale Mating NearMating Consortship
## 1 Y Y N N N
## 2 Y Y N N N
## 3 Y Y N N Y
## 4 Y Y Y Y Y
## 5 Y Y Y Y Y
## 6 Y Y Y Y N
## ObservationMinutes FollowType FollowTypeII Lengthofmating PelvicThrusts
## 1 731 F full F NA NA
## 2 712 F full F NA NA
## 3 788 F full F NA NA
## 4 667 F full F 0.003785 NA
## 5 667 F full F 0.004074 NA
## 6 806 F full F NA NA
## Male MaletypeI MaleTypeII MaleTypeIII MatingTypeExcel
## 1 Jari Manis Flanged Prime Male Prime
## 2 Jari Manis Flanged Prime Male Prime
## 3 Subadult H Unflanged Unflanged Non-Prime
## 4 Jari Manis Flanged Prime Male Prime Cooperative
## 5 Jari Manis Flanged Prime Male Prime Cooperative
## 6 Jari Manis Flanged Prime Male Prime
## MatingTypePercent MatingTypePercentII MatingTypeII MatingTypeIII
## 1
## 2
## 3
## 4 Cooperative Cooperative Unforced Unresisted
## 5 Cooperative Cooperative Unforced Unresisted
## 6 Forced Forced
## Matingtypequestionable TotalnonresistantProceptiveandResistanceBehaviors
## 1 NA
## 2 NA
## 3 NA
## 4 No 1
## 5 No 2
## 6 1
## NonResistantBeforeBehaviors NonResistantDuringBehaviors
## 1 NA NA
## 2 NA NA
## 3 NA NA
## 4 1 NA
## 5 NA 1
## 6 NA NA
## Totalnonresistantbehaviors NonresistantBehaviors ProceptiveBehaviors
## 1 NA NA NA
## 2 NA NA NA
## 3 NA NA NA
## 4 1 1.0 NA
## 5 1 0.5 NA
## 6 NA 0.0 NA
## ProceptiveBehaviors_A TotalunresistedandProcepetivebehaviors
## 1 NA NA
## 2 NA NA
## 3 NA NA
## 4 0 1
## 5 0 1
## 6 0 NA
## unresistedandProcepetivebehaviors ResistantBeforeBehaviors
## 1 NA NA
## 2 NA NA
## 3 NA NA
## 4 1.0 NA
## 5 0.5 1
## 6 0.0 1
## BeforeBehaviors ResistantDuringBehaviors DuringBehaviors
## 1 NA NA NA
## 2 NA NA NA
## 3 NA NA NA
## 4 0.0 NA 0
## 5 0.5 NA 0
## 6 1.0 NA 0
## TotalResistantBehaviors ResistantBehaviors AttractionBehaviors Mating01
## 1 NA NA NA 0
## 2 NA NA NA 0
## 3 NA NA NA 0
## 4 NA 0.0 2 1
## 5 1 0.5 3 1
## 6 1 1.0 1 0
## POP preg lact prime past unflanged included endomiss
## 1 1 0 0 1 0 0 1 0
## 2 1 0 0 1 0 0 1 0
## 3 1 0 0 0 0 1 1 0
## 4 1 0 0 1 0 0 1 0
## 5 1 0 0 1 0 0 1 0
## 6 1 0 0 1 0 0 1 0I created 6 more variables which serve as each entry of the replicated bar plot. The 3 female ovulatory statuses stay the same, though they are divided bewtween those who encountered prime males and those who encountered non-prime males (either unflanged or past-prime). For each variable I also counted the number of occurences of encounters by counting the number of rows. This is the same as what I did in the first replication.
nonPOP_nonprime <- filter(nonPOP, prime == "0")
totalmatings1 <- length(attributes(nonPOP_nonprime)$row.names)nonPOP_prime <- filter(nonPOP, prime == "1")
totalmatings2 <- length(attributes(nonPOP_prime)$row.names)pregnant_nonprime <- filter(pregnant, prime == "0")
totalmatings3 <- length(attributes(pregnant_nonprime)$row.names)pregnant_prime <- filter(pregnant, prime == "1")
totalmatings4 <- length(attributes(pregnant_prime)$row.names)POP_nonprime <- filter(POP, prime == "0")
totalmatings5 <- length(attributes(POP_nonprime)$row.names)POP_prime <- filter(POP, prime == "1")
totalmatings6 <- length(attributes(POP_prime)$row.names)I then created a vector of each of the values of occurences, as I did before, and created a bar plot.
v2 <- c(totalmatings1, totalmatings2, totalmatings3, totalmatings4, totalmatings5, totalmatings6)
barplot(v2, main = "Figure 2", xlab = "female endocrine status", ylab = "encounters observed", names.arg = c("non-periovulatory","non-periovulatory","pregnant","pregnant", "periovulatory", "periovulatory"), col = "black", density = c(100, 0, 100, 0, 100, 0), space = c(1, 0, 1, 0, 1, 0))This is what the figure from the paper looks like:
This replication did not turn out the same as the original figure. The “pregnant” and “periovulatory” categories look correct, though the values for the “non-periovulatory” category are too low. Unsure how to fix this, I tried using a different file, which contained a shortened version of the interaction data.
Attempt 2
I loaded in the other converted file (which contains fewer observation entries) and created a new dataframe which only includes the observations which are in the final analysis:
# getting the data into R
library(curl)
f2 <- curl("https://raw.githubusercontent.com/natalierobinson96/data-reanalysis-assignment/master/interactions%20(short).csv")
d2 <- read.csv(f2, header = TRUE, sep = ",")
d2 <- filter(d2, InteractionwithMale == "Y")
head(d2)## Date Name Name2 Record. TypeofInteraction FemaleAgeclass
## 1 34183 Betina O Betina 668 Adol Female
## 2 34184 Betina O Betina 670 Consortship Adol Female
## 3 34337 Betina P Betina 862 Adult Female
## 4 34339 Betina P Betina 871 Consortship Adult Female
## 5 35529 Ceska Ceska 69 Sexual Interaction Adult Female
## 6 35529 Ceska Ceska 2858 Encounter Adult Female
## FemaleMotherClass OffspringClass ObservedReproductiveStatus
## 1 Adol Female
## 2 Adol Female
## 3 Mother
## 4 Mother
## 5 Mother Inf no juv
## 6 Mother Inf no juv
## EndocrineStatusHormones CyclePhaseHormones ReproductiveStatus Endo2
## 1 No Data No Data
## 2 No Data No Data
## 3 No Data No Data
## 4 No Data No Data
## 5 No Data No Data
## 6 No Data No Data
## Endo3 Endo4 Endo5 Endo6 Endo7 Endo8 Pregnantvs.nonpregnant
## 1 No Data No Data No Data No Data No Data No Data No Data
## 2 No Data No Data No Data No Data No Data No Data No Data
## 3 No Data No Data No Data No Data No Data No Data No Data
## 4 No Data No Data No Data No Data No Data No Data No Data
## 5 No Data No Data No Data No Data No Data No Data No Data
## 6 No Data No Data No Data No Data No Data No Data No Data
## E1CmgCr PDGmgCr CommentMET EndorineComment SocialInteraction
## 1 NA NA Y
## 2 NA NA Y
## 3 NA NA Y
## 4 NA NA Y
## 5 NA NA Y
## 6 NA NA Y
## InteractionwithMale Mating NearMating Consortship ObservationMinutes
## 1 Y N N N 310
## 2 Y N N Y 369
## 3 Y N N N 805
## 4 Y N N N 805
## 5 Y N Y Y 740
## 6 Y N Y Y 740
## FollowType FollowTypeII Lengthofmating PelvicThrusts
## 1 D found then full day NA NA
## 2 S Switched to another OH NA NA
## 3 L late full day F NA NA
## 4 L late full day F 0.171528 NA
## 5 F full F 0.239583 NA
## 6 F full F 0.006944 NA
## Male MaletypeI MaleTypeII MaleTypeIII MatingTypeExcel
## 1 Jari Manis Flanged Prime Male Prime
## 2 Jari Manis Flanged Prime Male Prime
## 3 Unflanged male Unflanged Unflanged Non-Prime
## 4 Unflanged Male Unflanged Unflanged Non-Prime
## 5 Fusuyo Flanged Prime Male Prime
## 6 Roman Flanged Past Prime Non-Prime
## MatingTypePercent MatingTypePercentII MatingTypeII MatingTypeIII
## 1
## 2
## 3
## 4
## 5
## 6
## Matingtypequestionable TotalnonresistantProceptiveandResistanceBehaviors
## 1 0
## 2 0
## 3 0
## 4 0
## 5 0
## 6 0
## NonResistantBeforeBehaviors NonResistantDuringBehaviors
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Totalnonresistantbehaviors NonresistantBehaviors ProceptiveBehaviors
## 1 0 NA 0
## 2 0 NA 0
## 3 0 NA 0
## 4 0 NA 0
## 5 0 NA 0
## 6 0 NA 0
## ProceptiveBehaviors_A TotalunresistedandProcepetivebehaviors
## 1 NA 0
## 2 NA 0
## 3 NA 0
## 4 NA 0
## 5 NA 0
## 6 NA 0
## unresistedandProcepetivebehaviors ResistantBeforeBehaviors
## 1 NA 0
## 2 NA 0
## 3 NA 0
## 4 NA 0
## 5 NA 0
## 6 NA 0
## BeforeBehaviors ResistantDuringBehaviors DuringBehaviors
## 1 NA 0 NA
## 2 NA 0 NA
## 3 NA 0 NA
## 4 NA 0 NA
## 5 NA 0 NA
## 6 NA 0 NA
## TotalResistantBehaviors ResistantBehaviors AttractionBehaviors
## 1 0 NA 0
## 2 0 NA 0
## 3 0 NA 0
## 4 0 NA 0
## 5 0 NA 0
## 6 0 NA 0
## MaleInspectionBehaviors AttractionAggression TotalAggressiveBehaviors
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## TotalJuvenileDistressBeforeBehaviors
## 1 0
## 2 0
## 3 0
## 4 0
## 5 0
## 6 0
## TotalJuvenileDistressDuringBehaviors
## 1 0
## 2 0
## 3 0
## 4 0
## 5 0
## 6 0
## NonResistantBeforeFemaleandmalesidebyside
## 1 0
## 2 0
## 3 0
## 4 0
## 5 0
## 6 0
## NonResistantBeforeFemaleandmaleenternesttogether
## 1 0
## 2 0
## 3 0
## 4 0
## 5 0
## 6 0
## NonResistantBeforeFemalenotangrytowardsmale
## 1 0
## 2 0
## 3 0
## 4 0
## 5 0
## 6 0
## NonResistantDuringFemalecooperatiesatbeginningofmating
## 1 0
## 2 0
## 3 0
## 4 0
## 5 0
## 6 0
## NonResistantDuringFemalecooperatesaftermatingbegins
## 1 0
## 2 0
## 3 0
## 4 0
## 5 0
## 6 0
## ProceptiveBeforeFemaleApproachesMale
## 1 0
## 2 0
## 3 0
## 4 0
## 5 0
## 6 0
## ProceptiveDuringFemalehelpswithintromission
## 1 0
## 2 0
## 3 0
## 4 0
## 5 0
## 6 0
## ProceptiveDuringFemaletouchesmalegenitaliawithmouth
## 1 0
## 2 0
## 3 0
## 4 0
## 5 0
## 6 0
## ProceptiveDuringFemalelickspenis ProceptiveDuringFemaleputspenisinmouth
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## ProceptiveDuringFemalespreadsmaleslegs
## 1 0
## 2 0
## 3 0
## 4 0
## 5 0
## 6 0
## ProceptiveDuringFemalesitsontopofmale
## 1 0
## 2 0
## 3 0
## 4 0
## 5 0
## 6 0
## ProceptiveDuringFemalepullsmalesleginsolicitation
## 1 0
## 2 0
## 3 0
## 4 0
## 5 0
## 6 0
## ProceptiveDuringFemalepullsmalesarminsolicitation
## 1 0
## 2 0
## 3 0
## 4 0
## 5 0
## 6 0
## ProceptiveDuringFemaleperformspelvicthrusts ProceptiveDuringFemalecoos
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## ProceptiveDuringFemalelaysdownontopofmale
## 1 0
## 2 0
## 3 0
## 4 0
## 5 0
## 6 0
## ProceptiveDuringFemaleputslegontopofmale
## 1 0
## 2 0
## 3 0
## 4 0
## 5 0
## 6 0
## ProceptiveDuringFemalemalefacetoface
## 1 0
## 2 0
## 3 0
## 4 0
## 5 0
## 6 0
## ProceptiveDuringFemaledisplaysgenitalstomale
## 1 0
## 2 0
## 3 0
## 4 0
## 5 0
## 6 0
## ResistanceBeforeFemaleafraid ResistanceBeforeFemaleangry
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## ResistanceBeforeFemalevocalizesagainistmaleapproach
## 1 0
## 2 0
## 3 0
## 4 0
## 5 0
## 6 0
## ResistanceBeforeFemalemakessmallnoise ResistanceBeforeFemalelork
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## ResistanceBeforeFemalegroan ResistanceBeforeFemaledistressvocalization
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## ResistanceBeforeFemalevocalizesgutteralnoise
## 1 0
## 2 0
## 3 0
## 4 0
## 5 0
## 6 0
## ResistanceBeforeFemalerunsawayformmale
## 1 0
## 2 0
## 3 0
## 4 0
## 5 0
## 6 0
## ResistanceBeforeFemalemovesawayfrommale
## 1 0
## 2 0
## 3 0
## 4 0
## 5 0
## 6 0
## ResistanceBeforeFemaleaggressivetowardsmale
## 1 0
## 2 0
## 3 0
## 4 0
## 5 0
## 6 0
## ResistanceBeforeFemaleurinates ResistanceDuringafraid
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## ResistanceDuringangry ResistanceDuringmakessamllnoise
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## ResistanceDuringlork ResistanceDuringgroan
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## ResistanceDuringdistressvocalization ResistanceDuringgiutteralnoise
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## ResistanceDuringFemaletrystopullaway
## 1 0
## 2 0
## 3 0
## 4 0
## 5 0
## 6 0
## ResistanceDuringfemalestrugglesaganstmale
## 1 0
## 2 0
## 3 0
## 4 0
## 5 0
## 6 0
## ResistanceDuringFemaleaggressivetowardsmaleduringmating
## 1 0
## 2 0
## 3 0
## 4 0
## 5 0
## 6 0
## ResistanceDuringfemaleurinates AttractionMaleapproachesfemale
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## AttractionMaletouchesfemalegenitaliawithmouth
## 1 0
## 2 0
## 3 0
## 4 0
## 5 0
## 6 0
## AttractionMalesniffsfemalegenitals
## 1 0
## 2 0
## 3 0
## 4 0
## 5 0
## 6 0
## AttractionMaletouchesopensfemalegenitals
## 1 0
## 2 0
## 3 0
## 4 0
## 5 0
## 6 0
## AttractionMalesmellskissesfemalehand AttractionMalerubsfemalenongenitals
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## AttractionMalelicksspermfromfemalegenitalia
## 1 0
## 2 0
## 3 0
## 4 0
## 5 0
## 6 0
## AttractionMalespreadsfemaleslegsforinspection
## 1 0
## 2 0
## 3 0
## 4 0
## 5 0
## 6 0
## AttractionMalemoansduringmating AggressionsMaleforcesapartfemalelegs
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## AggressionsMaleholdsfemale AggressionsMalechasesfemale
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## AggressionsMalegrabsfemale AggressionsMalepullsfemalelegfoot
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## AggressionsMalepullsfemalearmhand AggressionsMalepullsfemaledown
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## AggressionsMalehitsfemaleleg AggressionsMalebitesfemalelightly
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## AggressionsMalehitsfemalelightly AggressionsMaleholdsfemaleonlap
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## AggressionsMaleembracesfemale AggressionsMalepositionsfemaleformating
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## AggressionsMalesitsontopoffemale AggressionsMalelaysdownontopoffemale
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## AggressionMalegrumphs AggressionMalekissgrunts Juvenilebeforeafraid
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Juvenilebeforeangry Juvenilebeforecrys Juvenilebeforevocalizes
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Juvenilebeforeurinates Juvenilebeforemaletouchesjuvenile
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Juvenilebeforemalepullsjuvenile JuvenileDuringJuvenilehitsmale
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## JuvenileDuringJuvenilebitesmale
## 1 0
## 2 0
## 3 0
## 4 0
## 5 0
## 6 0
## JuvenileDuringJuveniletriestopullmaleofffemale
## 1 0
## 2 0
## 3 0
## 4 0
## 5 0
## 6 0
## JuvenileDuringJuveniletriestointerfere JuvenileDuringafraid
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## JuvenileDuringangry JuvenileDuringcrys JuvenileDuringvocalizes
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## JuvenileDuringurinates JuvenileDuringmaletouchesjuvenile
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## JuvenileDuringmalepushesjuvenileaway Malehangingbelowfemale
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Malehangsabovefemale Femalelyingdownundermale Femalesitsbelowmale
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Malerejectsfemaleadvance Mating01 maletype2 male2miss femendo prime POP
## 1 0 0 Prime Male 3 NA 1 NA
## 2 0 0 Prime Male 3 NA 1 NA
## 3 0 0 Unflanged 1 NA 0 NA
## 4 0 0 Unflanged 1 NA 0 NA
## 5 0 0 Prime Male 3 NA 1 NA
## 6 0 0 Past Prime 2 NA NA NA
## nPOP CYCN POPprime nPOPprime POPnprime nPOPnprime CYCprime CYCNprime
## 1 NA NA NA NA 0 0 NA NA
## 2 NA NA NA NA 0 0 NA NA
## 3 NA NA 0 0 NA NA 0 0
## 4 NA NA 0 0 NA NA 0 0
## 5 NA NA NA NA 0 0 NA NA
## 6 NA NA NA NA NA NA NA NA
## POPint nPOPint filter_. preglact
## 1 NA NA 1 NA
## 2 NA NA 1 NA
## 3 0 0 1 NA
## 4 0 0 1 NA
## 5 NA NA 1 NA
## 6 NA NA 1 NAThis file already had the interactions divided into columns of the 6 categories for the barplot, so I filtered these out.
nonPOP_nonprime <- filter(d2, nPOPnprime == "1")
totalmatings1 <- length(attributes(nonPOP_nonprime)$row.names)nonPOP_prime <- filter(d2, nPOPprime == "1")
totalmatings2 <- length(attributes(nonPOP_prime)$row.names)pregnant_nonprime <- filter(d2, CYCNprime == "1")
totalmatings3 <- length(attributes(pregnant_nonprime)$row.names)pregnant_prime <- filter(d2, CYCprime == "1")
totalmatings4 <- length(attributes(pregnant_prime)$row.names)POP_nonprime <- filter(d2, POPnprime == "1")
totalmatings5 <- length(attributes(POP_nonprime)$row.names)POP_prime <- filter(d2, POPprime == "1")
totalmatings6 <- length(attributes(POP_prime)$row.names)I then created a vector of each of the values of occurences, and created a bar plot.
v2 <- c(totalmatings1, totalmatings2, totalmatings3, totalmatings4, totalmatings5, totalmatings6)
barplot(v2, main = "Figure 2", xlab = "female endocrine status", ylab = "encounters observed", names.arg = c("non-periovulatory","non-periovulatory","pregnant","pregnant", "periovulatory", "periovulatory"), col = "black", density = c(100, 0, 100, 0, 100, 0), space = c(1, 0, 1, 0, 1, 0))
Here, the “periovulatory” columns remain correct, the “pregnant” columns are now too high, and the “non-periovulatory” columns remain incorrect.
Attempt 3
After trying more iterations of code with both of the files, I noticed a column titled “endomiss” in the original long version of the interactions file. This column is filled in for each interaciton observation with either a “0” or “1”. My best guess is that this indicates whether or not the endocrine data from urine samples is missing or not. “0” would indicate that it is not missing (so the interaction can be used), while “1” would indicate that the endocrine data is missing, and should be excluded from the analysis. I added this into the filtering of my original code to see if it would help me.
First, I loaded the original set of data in to R.
library(curl)
f2 <- curl("https://raw.githubusercontent.com/natalierobinson96/data-reanalysis-assignment/master/interactions.csv")
d2 <- read.csv(f2, header = TRUE, sep = ",")Next I created a new dataframe which only includes the observation entries which were included in the final analysis.
d2 <- filter(d2, InteractionwithMale == "Y")
d2 <- filter(d2, included == "1")
d2 <- filter(d2, endomiss == "0")
head(d2) # I got it down to the 153 observations!!## Date day year year2 DateSPSS Name Name2 Record.
## 1 34340 7 94 94.01780 01/07/1994 Elizabeth Elizabeth 31
## 2 34343 10 94 94.02601 01/10/1994 Elizabeth Elizabeth 40
## 3 34309 341 93 93.93292 12/07/1993 Elizabeth Elizabeth 830
## 4 34342 9 94 94.02327 01/09/1994 Elizabeth Elizabeth 879
## 5 34344 11 94 94.02875 01/11/1994 Elizabeth Elizabeth 882
## 6 34380 47 94 94.12731 02/16/1994 Elizabeth Elizabeth 942
## TypeofInteraction FemaleAgeclass FemaleMotherClass
## 1 Mating 1 Adult Female Mother
## 2 Consortship Adult Female Mother
## 3 Adult Female Mother
## 4 Female companion consortship Adult Female Mother
## 5 Female companion consortship Adult Female Mother
## 6 Adult Female Mother
## OffspringClass ObservedReproductiveStatus EndocrineStatusHormones
## 1 Inf no juv L4
## 2 Inf no juv L4 cycling
## 3 Inf no juv L4 cycling
## 4 Inf no juv L4 cycling
## 5 Inf no juv L4 cycling
## 6 Inf no juv L4
## CyclePhaseHormones ReproductiveStatus Endo2 Endo3 Endo4 Endo5
## 1 Cycling non-POP Cycling POP POP
## 2 non-pop Cycling non-POP non-POP non-POP non-POP
## 3 non-pop Cycling non-POP non-POP non-POP non-POP
## 4 non-pop Cycling non-POP non-POP non-POP non-POP
## 5 non-pop Cycling non-POP non-POP non-POP non-POP
## 6 Cycling non-POP non-POP non-POP non-POP
## Endo6 Endo7 Endo8 Pregnantvs.nonpregnant E1CmgCr PDGmgCr
## 1 Near-POP POP non-POP Non-Preg NA NA
## 2 non-POP non-POP non-POP Non-Preg 14253 1803
## 3 non-POP non-POP non-POP Non-Preg 12658 308
## 4 non-POP non-POP non-POP Non-Preg 19305 3818
## 5 non-POP non-POP non-POP Non-Preg 22809 3678
## 6 non-POP non-POP non-POP Non-Preg NA NA
## CommentMET
## 1
## 2 luteal
## 3 low E & P
## 4 luteal
## 5 luteal
## 6
## EndorineComment
## 1 High P 6 Jan-16 Jan, no data when E peak occurred, looks just after ovulation
## 2
## 3
## 4
## 5
## 6
## SocialInteraction InteractionwithMale Mating NearMating Consortship
## 1 Y Y Y N Y
## 2 Y Y N N Y
## 3 Y Y N N N
## 4 Y Y N N N
## 5 Y Y N N N
## 6 Y Y N N don't know
## ObservationMinutes FollowType FollowTypeII Lengthofmating
## 1 785 L late full day F 0.001389
## 2 802 F full F NA
## 3 635 F full F NA
## 4 688 L late full day F NA
## 5 660 F full F NA
## 6 719 F full F NA
## PelvicThrusts Male MaletypeI MaleTypeII MaleTypeIII
## 1 10 Gagung Unflanged Unflanged Non-Prime
## 2 NA Unflanged Male Unflanged Unflanged Non-Prime
## 3 NA Roman Flanged Prime Male Prime
## 4 NA Unflanged male Unflanged Unflanged Non-Prime
## 5 NA Toby Unflanged Unflanged Non-Prime
## 6 NA Jari Manis Flanged Prime Male Prime
## MatingTypeExcel MatingTypePercent MatingTypePercentII MatingTypeII
## 1 Cooperative Cooperative Cooperative Unforced
## 2
## 3
## 4
## 5
## 6
## MatingTypeIII Matingtypequestionable
## 1 Unresisted No
## 2
## 3
## 4
## 5
## 6
## TotalnonresistantProceptiveandResistanceBehaviors
## 1 3
## 2 NA
## 3 NA
## 4 NA
## 5 NA
## 6 NA
## NonResistantBeforeBehaviors NonResistantDuringBehaviors
## 1 1 NA
## 2 NA NA
## 3 NA NA
## 4 NA NA
## 5 NA NA
## 6 NA NA
## Totalnonresistantbehaviors NonresistantBehaviors ProceptiveBehaviors
## 1 1 0.33 1
## 2 NA NA NA
## 3 NA NA NA
## 4 NA NA NA
## 5 NA NA NA
## 6 NA NA NA
## ProceptiveBehaviors_A TotalunresistedandProcepetivebehaviors
## 1 0.33 2
## 2 NA NA
## 3 NA NA
## 4 NA NA
## 5 NA NA
## 6 NA NA
## unresistedandProcepetivebehaviors ResistantBeforeBehaviors
## 1 0.67 1
## 2 NA NA
## 3 NA NA
## 4 NA NA
## 5 NA NA
## 6 NA NA
## BeforeBehaviors ResistantDuringBehaviors DuringBehaviors
## 1 0.33 NA 0
## 2 NA NA NA
## 3 NA NA NA
## 4 NA NA NA
## 5 NA NA NA
## 6 NA NA NA
## TotalResistantBehaviors ResistantBehaviors AttractionBehaviors Mating01
## 1 1 0.33 3 1
## 2 NA NA NA 0
## 3 NA NA NA 0
## 4 NA NA NA 0
## 5 NA NA NA 0
## 6 NA NA NA 0
## POP preg lact prime past unflanged included endomiss
## 1 0 0 0 0 0 1 1 0
## 2 0 0 0 0 0 1 1 0
## 3 0 0 0 1 0 0 1 0
## 4 0 0 0 0 0 1 1 0
## 5 0 0 0 0 0 1 1 0
## 6 0 0 0 1 0 0 1 0Finally, this dataframe contains 153 rows – the correct number of observations!
As before, I created the 3 categories of female endocrine status:
nonPOP <- filter(d2, preg == "0" & POP == "0") #matings with females not in periovulatory period; currently cyclingpregnant <- filter(d2, preg == "1") #matings with pregnant females POP <- filter(d2, POP == "1") #matings with females in periovulatory periodThen I created 6 more variables which will serve as each entry of the replicated bar plot. The 3 female ovulatory statuses stay the same, though they are divided bewtween those who encountered prime males and those who encountered non-prime males (either unflanged or past-prime). For each variable I also counted the number of occurences of encounters by counting the number of rows.
nonPOP_nonprime <- filter(nonPOP, prime == "0")
totalmatings1 <- length(attributes(nonPOP_nonprime)$row.names)nonPOP_prime <- filter(nonPOP, prime == "1")
totalmatings2 <- length(attributes(nonPOP_prime)$row.names)pregnant_nonprime <- filter(pregnant, prime == "0")
totalmatings3 <- length(attributes(pregnant_nonprime)$row.names)pregnant_prime <- filter(pregnant, prime == "1")
totalmatings4 <- length(attributes(pregnant_prime)$row.names)POP_nonprime <- filter(POP, prime == "0")
totalmatings5 <- length(attributes(POP_nonprime)$row.names)POP_prime <- filter(POP, prime == "1")
totalmatings6 <- length(attributes(POP_prime)$row.names)I then created a vector of each of the values of occurences, and plotted this.
v2 <- c(totalmatings1, totalmatings2, totalmatings3, totalmatings4, totalmatings5, totalmatings6)
barplot(v2, main = "Figure 2", xlab = "female endocrine status", ylab = "encounters observed", names.arg = c("non-periovulatory","non-periovulatory","pregnant","pregnant", "periovulatory", "periovulatory"), col = "black", density = c(100, 0, 100, 0, 100, 0), space = c(1, 0, 1, 0, 1, 0))
Again, this is what the figure from the paper looks like:
They are the same!
Though the code for creating this graph is simple, the difficult part of replication was sorting through all the data that was sent to me. I was given separate spreadsheets (of various different formats) from different dates containing different data. This made it very difficult to figure out which data I should use for each part of the replication. This has taught me an important lesson for my own future research/data collection. A cleaner, more simple dataset allows less room for error in analysis and is overall more user-friendly.
Linear Mixed Model Analysis (Inferential)
To better understand this data, Knott et al. (2010) used linear mixed model analyses. As explained in the paper, all random effects were intercept only and they used female identity as the subject identifier. Male identity was not used to characterize non-independence because male inclusion in the observations was not under researcher control. Additionally, female reproductive status varies substantially more than the male-specific variable of prime versus non-prime.
For continuous outcomes (such as the number of resistant behaviors observed), the authors used mixed linear models. This was done in an SPSS program called MIXED. I will use the lmer() function in the lme4 package. Since the number of behaviors in each category were skewed, log transformed variables were used for the analysis, and were later returned to linear values for the reporting of estimates (Figure 3 in the paper). The same modeling was done for the association between male and female behavior during mating interactions.
For binary outcomes (such as type of male encountered and the occurence of mating), mixed logistic models were used. The authors used the logit.mixed procedure in R, though this package is no longer available. Instead, I used the glmer() function in the lme4 package for my replication.
Linear Mixed Model Analysis of Female and Male Behaviors Observed During Copulation (Table 1)
Table 1 in the paper (below) shows the results of linear mixed model analyses of different female and male behaviors during mating. The behaviors in each category were skewed, so log-transformed variables were used for analysis. The authors used female reproductive status and male type as main effects. In separate models, the interaction of female reproductive status and male type were included to test the primary hypotheses. Results are reported as F statistics, meaning an ANOVA was done to test the significant differences between models. The rows indicate the type of behavior, and the columns indicate which variable was left in the model when being compared with the full model (with both female reproductive status and male type).
The authors decided that in order to investigate the distribution of male and female behaviors within matings, they needed to use a more inclusive dataset (rather than the 22 observations of mating where all reproductive statuses were known). Thus, observations of mating with unclassified female reproductive state were assumed to be non-periovulatory, as this is the state in which females are most likely in due to their long inter-birth intervals. This prevents having categories with zero observations and creates a larger sample size (n=42).
First I loaded in the dataset and altered the dataframe so that only the relevant columns were showing.
f <- curl("https://raw.githubusercontent.com/natalierobinson96/data-reanalysis-assignment/master/ohmatingspss2.csv")
d4 <- read.csv(f, header = TRUE, sep = ",")
d4 <- select(d4, FEMALE, FReproStatus, MALE, MALE.AGE.CLASS.I, TOTAL.PROCEPTIVE, TOTAL.RESISTANCE, MALE.AGGRESSION)
head(d4)## FEMALE FReproStatus
## 1 Elizabeth nonPOP
## 2 Marissa nonPOP
## 3 Marissa nonPOP
## 4 Marissa nonPOP
## 5 Marissa nonPOP
## 6 FL01Sep99 nonPOP
## MALE MALE.AGE.CLASS.I
## 1 Gagung Unflanged
## 2 Jari Manis Flanged
## 3 Jari Manis Flanged
## 4 Jari Manis Flanged
## 5 UML31Jan95 Unflanged
## 6 Dony Unflanged
## TOTAL.PROCEPTIVE TOTAL.RESISTANCE MALE.AGGRESSION
## 1 1 1 1
## 2 1 NA 4
## 3 1 1 2
## 4 1 1 3
## 5 NA 1 NA
## 6 NA 1 NAsummary(d4)## FEMALE FReproStatus
## Marissa :10 nonPOP :31
## Kristen : 6 POP : 4
## Kayla : 4 Pregnant: 7
## Zarina : 4
## Shea : 3
## AF10Jan98\v : 2
## (Other) :13
## MALE
## Jari Manis : 8
## Roman : 4
## Wendell : 4
## Male V : 3
## Rob : 3
## Dony : 2
## (Other) :18
## MALE.AGE.CLASS.I TOTAL.PROCEPTIVE TOTAL.RESISTANCE
## Flanged :17 Min. :1.000 Min. : 1
## Unflanged :25 1st Qu.:1.000 1st Qu.: 1
## Median :1.000 Median : 1
## Mean :1.783 Mean : 2
## 3rd Qu.:1.500 3rd Qu.: 2
## Max. :8.000 Max. :10
## NA's :19 NA's :23
## MALE.AGGRESSION
## Min. :1.000
## 1st Qu.:1.000
## Median :2.000
## Mean :2.591
## 3rd Qu.:3.750
## Max. :7.000
## NA's :20As done in Module 18, I began with some exploratory visualization. First, I looked at the proceptive behaviors from females in relation to each individual female. This plot shows that there is a lot of variation between the number of proceptive behaviors observed between different females. This is why the female ID was treated as a random effect.
par(mfrow = c(1, 1))
boxplot(data = d4, TOTAL.PROCEPTIVE ~ FEMALE, col = c("lightpink1"))
Variation can also be seen in number of resistance behaviors between females.
par(mfrow = c(1, 1))
boxplot(data = d4, TOTAL.RESISTANCE ~ FEMALE, col = c("lightblue1"))
More proceptive behaviors were directed towards flanged males.
boxplot(data = d4, TOTAL.PROCEPTIVE ~ MALE.AGE.CLASS.I, col = c("burlywood2", "lightpink1"))
This figure confirms that the most proceptive behaviors were shown by pregnant females (the median is way higher) as stated in the paper.
boxplot(data = d4, TOTAL.PROCEPTIVE ~ FReproStatus, col = c("burlywood2", "lightpink1"))
boxplot(data = d4, TOTAL.RESISTANCE ~ FReproStatus * MALE.AGE.CLASS.I, col = c("burlywood2", "lightpink1", "lightgreen", "lightblue"))
The paper also states that, in contrast to other reports, they found male aggressive behaviors in the context of mating were performed more often by prime than non-prime males. This trend can be seen in the following plot:
par(mfrow = c(1, 1))
boxplot(data = d4, MALE.AGGRESSION ~ MALE.AGE.CLASS.I, col = c("lightpink1", "lightblue"))
They explained that these behaviors were not directed significantly more towards any particular female reproductive class, as seen below:
par(mfrow = c(1, 1))
boxplot(data = d4, MALE.AGGRESSION ~ FReproStatus, col = c("lightgreen", "lightblue"))
Then I began the replication of the first row of Table 1 in which the response variable is “female proceptive behaviours”.
I began by creating the full model, in which both female reproductive status and male type are included as main effects. I also took the log of the number of resistant behaviors, as the authors explained they used log-transformed variables (reproductive status and male type are categorical, so the log can not be done on those variables).
full <- lmer(data = d4, log(TOTAL.PROCEPTIVE) ~ FReproStatus + MALE.AGE.CLASS.I + (1 | FEMALE))
summary(full)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: log(TOTAL.PROCEPTIVE) ~ FReproStatus + MALE.AGE.CLASS.I + (1 |
## FEMALE)
## Data: d4
##
## REML criterion at convergence: 38.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.5078 -0.3251 -0.0887 0.3500 2.5414
##
## Random effects:
## Groups Name Variance Std.Dev.
## FEMALE (Intercept) 0.05705 0.2389
## Residual 0.26372 0.5135
## Number of obs: 23, groups: FEMALE, 11
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 0.2359 0.2193 12.3790
## FReproStatusPOP 0.4539 0.3789 18.9990
## FReproStatusPregnant 0.7320 0.2991 17.6900
## MALE.AGE.CLASS.IUnflanged -0.1805 0.2625 17.1060
## t value Pr(>|t|)
## (Intercept) 1.075 0.3027
## FReproStatusPOP 1.198 0.2457
## FReproStatusPregnant 2.447 0.0251 *
## MALE.AGE.CLASS.IUnflanged -0.688 0.5010
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) FRSPOP FRprSP
## FRprSttsPOP -0.472
## FRprSttsPrg -0.263 0.095
## MALE.AGE.CL -0.690 0.380 -0.099Then I created a reduced model, in which only the female reproductive status is considered.
reduced1 <- lmer(data = d4, log(TOTAL.PROCEPTIVE) ~ FReproStatus + (1 | FEMALE))
summary(reduced1)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: log(TOTAL.PROCEPTIVE) ~ FReproStatus + (1 | FEMALE)
## Data: d4
##
## REML criterion at convergence: 38
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.4589 -0.2893 -0.0335 0.2873 2.5830
##
## Random effects:
## Groups Name Variance Std.Dev.
## FEMALE (Intercept) 0.04329 0.2081
## Residual 0.26468 0.5145
## Number of obs: 23, groups: FEMALE, 11
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.1259 0.1537 12.7480 0.819 0.4278
## FReproStatusPOP 0.5611 0.3463 19.9430 1.620 0.1209
## FReproStatusPregnant 0.7124 0.2919 18.8360 2.441 0.0247 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) FRSPOP
## FRprSttsPOP -0.331
## FRprSttsPrg -0.469 0.155Finally, I did an ANOVA comparing the full and reduced models in order to obtain the F-Statistic.
anova(reduced1, full, test = "F")## refitting model(s) with ML (instead of REML)## Data: d4
## Models:
## object: log(TOTAL.PROCEPTIVE) ~ FReproStatus + (1 | FEMALE)
## ..1: log(TOTAL.PROCEPTIVE) ~ FReproStatus + MALE.AGE.CLASS.I + (1 |
## ..1: FEMALE)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## object 5 44.506 50.184 -17.253 34.506
## ..1 6 46.015 52.828 -17.008 34.015 0.4914 1 0.4833This ANOVA (using the anova() function in the {stats} package) provided me with a Chi squared value rather than an F-Statistic (as used in the paper).
I then used the Anova() function in the {car} package, which allowed me to obtain the F-Statistic.
library(car)
Anova(reduced1, full, type = c("II", "III", 2, 3), test = "F")## Analysis of Deviance Table (Type II Wald F tests with Kenward-Roger df)
##
## Response: log(TOTAL.PROCEPTIVE)
## F Df Df.res Pr(>F)
## FReproStatus 3.0642 2 18.085 0.07144 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1This result is different than what was found in the paper. It should be F(2,35.4) = 12.9, p < 0.001 (which is displayed in the first row and first column of Table 1)
I then created a second recuded model in which only the male type is considered.
reduced2 <- lmer(data = d4, log(TOTAL.RESISTANCE) ~ MALE.AGE.CLASS.I + (1 | FEMALE))
summary(reduced2)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: log(TOTAL.RESISTANCE) ~ MALE.AGE.CLASS.I + (1 | FEMALE)
## Data: d4
##
## REML criterion at convergence: 38.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.5642 -0.5259 -0.4173 0.2522 2.3525
##
## Random effects:
## Groups Name Variance Std.Dev.
## FEMALE (Intercept) 0.1877 0.4332
## Residual 0.3045 0.5518
## Number of obs: 19, groups: FEMALE, 10
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 0.39620 0.25490
## MALE.AGE.CLASS.IUnflanged 0.07285 0.28727
## df t value Pr(>|t|)
## (Intercept) 11.25300 1.554 0.148
## MALE.AGE.CLASS.IUnflanged 15.38500 0.254 0.803
##
## Correlation of Fixed Effects:
## (Intr)
## MALE.AGE.CL -0.610I ran another ANOVA of the full model and the second recuded model.
Anova(reduced2, full, type = c("II", "III", 2, 3), test = "F")## Analysis of Deviance Table (Type II Wald F tests with Kenward-Roger df)
##
## Response: log(TOTAL.RESISTANCE)
## F Df Df.res Pr(>F)
## MALE.AGE.CLASS.I 0.0545 1 15.315 0.8185This result is also different than what was found in the paper. It should be F(1,21.1) = 2.7, p = 0.117 (as reported in the first row and second column of Table 1).
Next I created new models for female resistant behaviors. These F-Statistics are reported in the second row of Table 1.
I began with a new full model, taking the log of the total number of resistant behaviors.
full <- lmer(data = d4, log(TOTAL.RESISTANCE) ~ FReproStatus + MALE.AGE.CLASS.I + (1 | FEMALE))
summary(full)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: log(TOTAL.RESISTANCE) ~ FReproStatus + MALE.AGE.CLASS.I + (1 |
## FEMALE)
## Data: d4
##
## REML criterion at convergence: 37.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.5315 -0.5285 -0.4127 0.2740 2.3289
##
## Random effects:
## Groups Name Variance Std.Dev.
## FEMALE (Intercept) 0.1847 0.4298
## Residual 0.3293 0.5738
## Number of obs: 19, groups: FEMALE, 10
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 0.3697 0.2902 12.5610
## FReproStatusPOP 0.1084 0.4318 12.2380
## MALE.AGE.CLASS.IUnflanged 0.1037 0.3339 15.5570
## t value Pr(>|t|)
## (Intercept) 1.274 0.226
## FReproStatusPOP 0.251 0.806
## MALE.AGE.CLASS.IUnflanged 0.311 0.760
##
## Correlation of Fixed Effects:
## (Intr) FRSPOP
## FRprSttsPOP -0.444
## MALE.AGE.CL -0.693 0.459I created a reduced model in which only the female reproductive status is considered.
reduced1 <- lmer(data = d4, log(TOTAL.RESISTANCE) ~ FReproStatus + (1 | FEMALE))
summary(reduced1)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: log(TOTAL.RESISTANCE) ~ FReproStatus + (1 | FEMALE)
## Data: d4
##
## REML criterion at convergence: 37.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.5425 -0.5141 -0.4528 0.3610 2.3699
##
## Random effects:
## Groups Name Variance Std.Dev.
## FEMALE (Intercept) 0.1569 0.3961
## Residual 0.3212 0.5667
## Number of obs: 19, groups: FEMALE, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.43368 0.20033 9.27300 2.165 0.0577 .
## FReproStatusPOP 0.05085 0.37739 12.03500 0.135 0.8950
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## FRprSttsPOP -0.206I ran an ANOVA to compare these two models
Anova(reduced1, full, type = c("II", "III", 2, 3), test = "F")## Analysis of Deviance Table (Type II Wald F tests with Kenward-Roger df)
##
## Response: log(TOTAL.RESISTANCE)
## F Df Df.res Pr(>F)
## FReproStatus 0.0161 1 11.312 0.9012This result is different than what was found in the paper. It should be F(2,42) = 4.2, p = 0.021.
I then created the second reduced model in which only male type is included.
reduced2 <- lmer(data = d4, log(TOTAL.RESISTANCE) ~ MALE.AGE.CLASS.I + (1 | FEMALE))
summary(reduced2)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: log(TOTAL.RESISTANCE) ~ MALE.AGE.CLASS.I + (1 | FEMALE)
## Data: d4
##
## REML criterion at convergence: 38.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.5642 -0.5259 -0.4173 0.2522 2.3525
##
## Random effects:
## Groups Name Variance Std.Dev.
## FEMALE (Intercept) 0.1877 0.4332
## Residual 0.3045 0.5518
## Number of obs: 19, groups: FEMALE, 10
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 0.39620 0.25490
## MALE.AGE.CLASS.IUnflanged 0.07285 0.28727
## df t value Pr(>|t|)
## (Intercept) 11.25300 1.554 0.148
## MALE.AGE.CLASS.IUnflanged 15.38500 0.254 0.803
##
## Correlation of Fixed Effects:
## (Intr)
## MALE.AGE.CL -0.610Anova(reduced2, full, type = c("II", "III", 2, 3), test = "F")## Analysis of Deviance Table (Type II Wald F tests with Kenward-Roger df)
##
## Response: log(TOTAL.RESISTANCE)
## F Df Df.res Pr(>F)
## MALE.AGE.CLASS.I 0.0545 1 15.315 0.8185This result is different than what was found in the paper. It should be F(1,42) = 2.7, p = 0.110.
I could not find the observations of male inspection behaviors in the data I was given, so was unable to attempt the replication of this model (the third row of Table 1).
Finally, I repeated the above steps for the male aggresive behaviors (the last row of Table 1).
full <- lmer(data = d4, log(MALE.AGGRESSION) ~ FReproStatus + MALE.AGE.CLASS.I + (1 | FEMALE))
summary(full)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: log(MALE.AGGRESSION) ~ FReproStatus + MALE.AGE.CLASS.I + (1 |
## FEMALE)
## Data: d4
##
## REML criterion at convergence: 42.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.41623 -0.87688 -0.03682 0.64080 1.51926
##
## Random effects:
## Groups Name Variance Std.Dev.
## FEMALE (Intercept) 0.0000 0.0000
## Residual 0.4394 0.6629
## Number of obs: 22, groups: FEMALE, 10
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 0.9388 0.2277 18.0000
## FReproStatusPOP -0.2457 0.4453 18.0000
## FReproStatusPregnant -0.8404 0.4956 18.0000
## MALE.AGE.CLASS.IUnflanged -0.1968 0.3046 18.0000
## t value Pr(>|t|)
## (Intercept) 4.123 0.000639 ***
## FReproStatusPOP -0.552 0.587995
## FReproStatusPregnant -1.696 0.107186
## MALE.AGE.CLASS.IUnflanged -0.646 0.526319
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) FRSPOP FRprSP
## FRprSttsPOP -0.511
## FRprSttsPrg -0.242 0.124
## MALE.AGE.CL -0.708 0.362 0.018The first reduced model:
reduced1 <- lmer(data = d4, log(MALE.AGGRESSION) ~ FReproStatus + (1 | FEMALE))
summary(reduced1)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: log(MALE.AGGRESSION) ~ FReproStatus + (1 | FEMALE)
## Data: d4
##
## REML criterion at convergence: 42.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.2788 -0.8507 0.0000 0.7351 1.7028
##
## Random effects:
## Groups Name Variance Std.Dev.
## FEMALE (Intercept) 8.893e-18 2.982e-09
## Residual 4.260e-01 6.527e-01
## Number of obs: 22, groups: FEMALE, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.8346 0.1583 19.0000 5.273 4.34e-05 ***
## FReproStatusPOP -0.1414 0.4087 19.0000 -0.346 0.733
## FReproStatusPregnant -0.8346 0.4879 19.0000 -1.711 0.103
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) FRSPOP
## FRprSttsPOP -0.387
## FRprSttsPrg -0.324 0.126I used the Anova() function in the {car} package to obtain the F-Statistic.
library(car)
Anova(reduced1, full, type = c("II", "III", 2, 3), test = "F")## Analysis of Deviance Table (Type II Wald F tests with Kenward-Roger df)
##
## Response: log(MALE.AGGRESSION)
## F Df Df.res Pr(>F)
## FReproStatus 1.2051 2 17.516 0.3233This result is different than what was found in the paper. It should be F(2,42) = 0, p = 0.966.
The second reduced model:
reduced2 <- lmer(data = d4, log(MALE.AGGRESSION) ~ MALE.AGE.CLASS.I + (1 | FEMALE))
summary(reduced2)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: log(MALE.AGGRESSION) ~ MALE.AGE.CLASS.I + (1 | FEMALE)
## Data: d4
##
## REML criterion at convergence: 46.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.18876 -0.96873 -0.05815 0.80154 1.67641
##
## Random effects:
## Groups Name Variance Std.Dev.
## FEMALE (Intercept) 0.0000 0.0000
## Residual 0.4613 0.6792
## Number of obs: 22, groups: FEMALE, 10
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 0.8074 0.1961 20.0000
## MALE.AGE.CLASS.IUnflanged -0.1494 0.2908 20.0000
## t value Pr(>|t|)
## (Intercept) 4.118 0.000534 ***
## MALE.AGE.CLASS.IUnflanged -0.514 0.612971
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## MALE.AGE.CL -0.674Another ANOVA:
Anova(reduced2, full, type = c("II", "III", 2, 3), test = "F")## Analysis of Deviance Table (Type II Wald F tests with Kenward-Roger df)
##
## Response: log(MALE.AGGRESSION)
## F Df Df.res Pr(>F)
## MALE.AGE.CLASS.I 0.2039 1 17.652 0.6571This result is different than what was found in the paper. It should be F(1,42) = 5.1, p = 0.029.
The authors also created separate models which include the interaction of female reproductive status and male type. In the code below, interaction terms are indicated with a colon between the two variables (“FReproStatus:MALE.AGE.CLASS.I”). I created models for female proceptive behaviors, female resistance behaviors, and male agressive behaviors.
full2 <- lmer(data = d4, TOTAL.PROCEPTIVE ~ FReproStatus:MALE.AGE.CLASS.I + (1 | FEMALE), REML = FALSE)## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficientssummary(full2)## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients## Linear mixed model fit by maximum likelihood t-tests use Satterthwaite
## approximations to degrees of freedom [lmerMod]
## Formula: TOTAL.PROCEPTIVE ~ FReproStatus:MALE.AGE.CLASS.I + (1 | FEMALE)
## Data: d4
##
## AIC BIC logLik deviance df.resid
## 90.4 98.4 -38.2 76.4 16
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8314 -0.1962 0.0000 0.0000 3.6629
##
## Random effects:
## Groups Name Variance Std.Dev.
## FEMALE (Intercept) 0.000 0.000
## Residual 1.623 1.274
## Number of obs: 23, groups: FEMALE, 11
##
## Fixed effects:
## Estimate
## (Intercept) 1.6667
## FReproStatusnonPOP:MALE.AGE.CLASS.IFlanged -0.6667
## FReproStatusPOP:MALE.AGE.CLASS.IFlanged 1.6667
## FReproStatusPregnant:MALE.AGE.CLASS.IFlanged 2.8333
## FReproStatusnonPOP:MALE.AGE.CLASS.IUnflanged -0.4167
## Std. Error
## (Intercept) 0.7356
## FReproStatusnonPOP:MALE.AGE.CLASS.IFlanged 0.8792
## FReproStatusPOP:MALE.AGE.CLASS.IFlanged 1.0403
## FReproStatusPregnant:MALE.AGE.CLASS.IFlanged 1.1630
## FReproStatusnonPOP:MALE.AGE.CLASS.IUnflanged 0.8625
## df
## (Intercept) 23.0000
## FReproStatusnonPOP:MALE.AGE.CLASS.IFlanged 23.0000
## FReproStatusPOP:MALE.AGE.CLASS.IFlanged 23.0000
## FReproStatusPregnant:MALE.AGE.CLASS.IFlanged 23.0000
## FReproStatusnonPOP:MALE.AGE.CLASS.IUnflanged 23.0000
## t value
## (Intercept) 2.266
## FReproStatusnonPOP:MALE.AGE.CLASS.IFlanged -0.758
## FReproStatusPOP:MALE.AGE.CLASS.IFlanged 1.602
## FReproStatusPregnant:MALE.AGE.CLASS.IFlanged 2.436
## FReproStatusnonPOP:MALE.AGE.CLASS.IUnflanged -0.483
## Pr(>|t|)
## (Intercept) 0.0332
## FReproStatusnonPOP:MALE.AGE.CLASS.IFlanged 0.4560
## FReproStatusPOP:MALE.AGE.CLASS.IFlanged 0.1228
## FReproStatusPregnant:MALE.AGE.CLASS.IFlanged 0.0230
## FReproStatusnonPOP:MALE.AGE.CLASS.IUnflanged 0.6336
##
## (Intercept) *
## FReproStatusnonPOP:MALE.AGE.CLASS.IFlanged
## FReproStatusPOP:MALE.AGE.CLASS.IFlanged
## FReproStatusPregnant:MALE.AGE.CLASS.IFlanged *
## FReproStatusnonPOP:MALE.AGE.CLASS.IUnflanged
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) FRprSttsnPOP:MALE.AGE.CLASS.IF
## FRprSttsnPOP:MALE.AGE.CLASS.IF -0.837
## FRprSttsPOP:MALE.AGE.CLASS.IFl -0.707 0.592
## FRSP:MALE.A -0.632 0.529
## FRSPOP:MALE.AGE.CLASS.IU -0.853 0.714
## FRprSttsPOP:MALE.AGE.CLASS.IFl FRSP:M
## FRprSttsnPOP:MALE.AGE.CLASS.IF
## FRprSttsPOP:MALE.AGE.CLASS.IFl
## FRSP:MALE.A 0.447
## FRSPOP:MALE.AGE.CLASS.IU 0.603 0.539
## fit warnings:
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficientsfull3 <- lmer(data = d4, TOTAL.RESISTANCE ~ FReproStatus:MALE.AGE.CLASS.I + (1 | FEMALE), REML = FALSE)## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficientssummary(full3)## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients## Linear mixed model fit by maximum likelihood t-tests use Satterthwaite
## approximations to degrees of freedom [lmerMod]
## Formula: TOTAL.RESISTANCE ~ FReproStatus:MALE.AGE.CLASS.I + (1 | FEMALE)
## Data: d4
##
## AIC BIC logLik deviance df.resid
## 91.0 95.7 -40.5 81.0 14
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.6535 -0.5381 -0.3654 0.1504 3.5314
##
## Random effects:
## Groups Name Variance Std.Dev.
## FEMALE (Intercept) 0.495 0.7036
## Residual 3.731 1.9317
## Number of obs: 19, groups: FEMALE, 10
##
## Fixed effects:
## Estimate
## (Intercept) 1.7994
## FReproStatusnonPOP:MALE.AGE.CLASS.IFlanged 0.6305
## FReproStatusPOP:MALE.AGE.CLASS.IFlanged 0.3907
## Std. Error
## (Intercept) 0.7023
## FReproStatusnonPOP:MALE.AGE.CLASS.IFlanged 1.0201
## FReproStatusPOP:MALE.AGE.CLASS.IFlanged 1.3199
## df
## (Intercept) 15.6340
## FReproStatusnonPOP:MALE.AGE.CLASS.IFlanged 18.8320
## FReproStatusPOP:MALE.AGE.CLASS.IFlanged 15.1940
## t value
## (Intercept) 2.562
## FReproStatusnonPOP:MALE.AGE.CLASS.IFlanged 0.618
## FReproStatusPOP:MALE.AGE.CLASS.IFlanged 0.296
## Pr(>|t|)
## (Intercept) 0.0212
## FReproStatusnonPOP:MALE.AGE.CLASS.IFlanged 0.5439
## FReproStatusPOP:MALE.AGE.CLASS.IFlanged 0.7712
##
## (Intercept) *
## FReproStatusnonPOP:MALE.AGE.CLASS.IFlanged
## FReproStatusPOP:MALE.AGE.CLASS.IFlanged
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) FRprSttsnPOP:MALE.AGE.CLASS.IF
## FRprSttsnPOP:MALE.AGE.CLASS.IF -0.610
## FRprSttsPOP:MALE.AGE.CLASS.IFl -0.424 0.330
## fit warnings:
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficientsfull4 <- lmer(data = d4, MALE.AGGRESSION ~ FReproStatus:MALE.AGE.CLASS.I + (1 | FEMALE), REML = FALSE)## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficientssummary(full4)## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients## Linear mixed model fit by maximum likelihood t-tests use Satterthwaite
## approximations to degrees of freedom [lmerMod]
## Formula: MALE.AGGRESSION ~ FReproStatus:MALE.AGE.CLASS.I + (1 | FEMALE)
## Data: d4
##
## AIC BIC logLik deviance df.resid
## 96.5 104.2 -41.3 82.5 15
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.3135 -0.7330 -0.1326 0.7928 1.6776
##
## Random effects:
## Groups Name Variance Std.Dev.
## FEMALE (Intercept) 0.7899 0.8887
## Residual 1.9145 1.3837
## Number of obs: 22, groups: FEMALE, 10
##
## Fixed effects:
## Estimate
## (Intercept) 1.6355
## FReproStatusnonPOP:MALE.AGE.CLASS.IFlanged 1.5404
## FReproStatusPOP:MALE.AGE.CLASS.IFlanged 0.8627
## FReproStatusPregnant:MALE.AGE.CLASS.IFlanged -2.1383
## FReproStatusnonPOP:MALE.AGE.CLASS.IUnflanged 0.8828
## Std. Error
## (Intercept) 1.5841
## FReproStatusnonPOP:MALE.AGE.CLASS.IFlanged 1.6381
## FReproStatusPOP:MALE.AGE.CLASS.IFlanged 1.8157
## FReproStatusPregnant:MALE.AGE.CLASS.IFlanged 2.1981
## FReproStatusnonPOP:MALE.AGE.CLASS.IUnflanged 1.6710
## df
## (Intercept) 21.9810
## FReproStatusnonPOP:MALE.AGE.CLASS.IFlanged 19.8500
## FReproStatusPOP:MALE.AGE.CLASS.IFlanged 20.9770
## FReproStatusPregnant:MALE.AGE.CLASS.IFlanged 20.1480
## FReproStatusnonPOP:MALE.AGE.CLASS.IUnflanged 21.9230
## t value
## (Intercept) 1.032
## FReproStatusnonPOP:MALE.AGE.CLASS.IFlanged 0.940
## FReproStatusPOP:MALE.AGE.CLASS.IFlanged 0.475
## FReproStatusPregnant:MALE.AGE.CLASS.IFlanged -0.973
## FReproStatusnonPOP:MALE.AGE.CLASS.IUnflanged 0.528
## Pr(>|t|)
## (Intercept) 0.313
## FReproStatusnonPOP:MALE.AGE.CLASS.IFlanged 0.358
## FReproStatusPOP:MALE.AGE.CLASS.IFlanged 0.640
## FReproStatusPregnant:MALE.AGE.CLASS.IFlanged 0.342
## FReproStatusnonPOP:MALE.AGE.CLASS.IUnflanged 0.603
##
## Correlation of Fixed Effects:
## (Intr) FRprSttsnPOP:MALE.AGE.CLASS.IF
## FRprSttsnPOP:MALE.AGE.CLASS.IF -0.920
## FRprSttsPOP:MALE.AGE.CLASS.IFl -0.857 0.839
## FRSP:MALE.A -0.705 0.700
## FRSPOP:MALE.AGE.CLASS.IU -0.941 0.889
## FRprSttsPOP:MALE.AGE.CLASS.IFl FRSP:M
## FRprSttsnPOP:MALE.AGE.CLASS.IF
## FRprSttsPOP:MALE.AGE.CLASS.IFl
## FRSP:MALE.A 0.621
## FRSPOP:MALE.AGE.CLASS.IU 0.840 0.671
## fit warnings:
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficientsI have tried endless variations of this code and still cannot figure out why I am not successfully replicating the analysis. While it is possible that I could have figured this out with more time, I have thought of a few possibilities of where I went wrong. I could be log-transforming the data incorrectly, I could be misinterpretting what Table 1 is meant to show, or I could be using the completely wrong spreadsheet of data. However, I have tried altering all of these factors, and am still unsuccessful. It is also possible that other effects went into the model (such as length of mating, number of pelvic thrusts, juvenile distress behaviors, or forced vs unforced mating) which were not properly explained in the methods section of the paper, and therefore I failed to include them. This is possible, as these variables are recorded in the dataset, though not explicitly mentioned in the paper.
Knott et al. (2010) stated that matings lasted an average of 7.9 minutes, with a range of 1-31 minutes.
I loaded in the same dataset I used for the LMM, without any of the prior modifications.
f <- curl("https://raw.githubusercontent.com/natalierobinson96/data-reanalysis-assignment/master/ohmatingspss2.csv")
d5 <- read.csv(f, header = TRUE, sep = ",")
mating_lengths <- d5$Length.of.Mating
mating_lengths## [1] 120 887 237 165 540 540 1140 250 360 165 540 600 540 450
## [15] 240 720 NA 540 360 300 540 360 60 117 600 600 165 180
## [29] 540 523 285 110 353 327 780 352 780 1860 1380 420 420 720It seems as though these values were recorded as seconds, so I will convert them to minutes.
mating_mins <- mating_lengths/60
mating_mins## [1] 2.000000 14.783333 3.950000 2.750000 9.000000 9.000000 19.000000
## [8] 4.166667 6.000000 2.750000 9.000000 10.000000 9.000000 7.500000
## [15] 4.000000 12.000000 NA 9.000000 6.000000 5.000000 9.000000
## [22] 6.000000 1.000000 1.950000 10.000000 10.000000 2.750000 3.000000
## [29] 9.000000 8.716667 4.750000 1.833333 5.883333 5.450000 13.000000
## [36] 5.866667 13.000000 31.000000 23.000000 7.000000 7.000000 12.000000Then I calculated the range and mean of these values.
range(mating_mins, na.rm = TRUE)## [1] 1 31mean(mating_mins, na.rm = TRUE)## [1] 8.197561Here, the range of the lengths of matings is 1-31, which matches what was stated in the paper. However, the average length was about 0.3 minutes longer than what was reported. I am confident that I properly calculated the average of this list of mating lengths, which leads me to believe I may be using the incorrect dataset for this set of analyses, and that is part of the reason I am not getting the same values.
Generalized Linear Mixed Model – Type of Male Encountered
As I was unsucessful with the previous model, I decided to attempt to replicate a GLMM used in the paper, despite not having a complete understanding of this topic. Unfortunately, I did not have time to finish this section of the replication.
First I loaded in the interactions dataset.
f3 <- curl("https://raw.githubusercontent.com/natalierobinson96/data-reanalysis-assignment/master/interactions.csv")
d3 <- read.csv(f3, header = TRUE, sep = ",")I filtered the data to only inlcude the interactions in which all reproductive statuses were known. I selected only the relevant columns for this analysis.
d3 <- filter(d3, endomiss == "0")
d3 <- filter(d3, included == "1")Since the first model has binary outcomes I wanted the observations to be recorded as “0” and “1” instead of “N” and “Y”, respectively.
SocialInteractionBinary <- revalue(d3$SocialInteraction, c("Y" = 1, "N" = 0, "Maybe" = 0, "partial" = 0, "Data missing" = 0, "Data Missing" = 0, "don't know" = 0, "Missing" = 0))
MaleInteractionBinary <- revalue(d3$InteractionwithMale, c("Y" = 1, "N" = 0, "C" = 0, "Maybe" = 0, "NN" = 0, "y" = 1))
MatingBinary <- revalue(d3$Mating, c("Y" = 1, "N" = 0, "?" = 0))
d3 <- data.frame(SocialInteractionBinary, MaleInteractionBinary, MatingBinary, d3)
d3 <- select(d3, Name2, SocialInteractionBinary, MaleInteractionBinary, MatingBinary, POP, preg, lact, prime, past, unflanged)
head(d3)## Name2 SocialInteractionBinary MaleInteractionBinary MatingBinary POP
## 1 Elizabeth 1 1 1 0
## 2 Elizabeth 1 1 0 0
## 3 Elizabeth 1 1 0 0
## 4 Elizabeth 1 1 0 0
## 5 Elizabeth 1 1 0 0
## 6 Elizabeth 1 1 0 0
## preg lact prime past unflanged
## 1 0 0 0 0 1
## 2 0 0 0 0 1
## 3 0 0 1 0 0
## 4 0 0 0 0 1
## 5 0 0 0 0 1
## 6 0 0 1 0 0Then I created a new column which indicated the female reproductive status
library(plyr)
d3$femrepo <- NA
for(i in 1:153) {
if(d3$POP[i] == 1) {
d3$femrepo[i] <- "POP"
}
}
for(i in 1:153) {
if(d3$preg[i] == 1) {
d3$femrepo[i] <- "preg"
}
}
for(i in 1:153) {
if(d3$POP[i] == 0 & d3$preg[i] == 0) {
d3$femrepo[i] <- "nonPOP"
}
}
d3$maletype <- NA
for(i in 1:153) {
if(d3$unflanged[i] == 1) {
d3$maletype[i] <- "unflanged"
}
}
for(i in 1:153) {
if(d3$unflanged[i] == 0) {
d3$maletype[i] <- "flanged"
}
}
d3 <- select(d3, Name2, SocialInteractionBinary, MaleInteractionBinary, MatingBinary, femrepo, maletype)
head(d3)## Name2 SocialInteractionBinary MaleInteractionBinary MatingBinary
## 1 Elizabeth 1 1 1
## 2 Elizabeth 1 1 0
## 3 Elizabeth 1 1 0
## 4 Elizabeth 1 1 0
## 5 Elizabeth 1 1 0
## 6 Elizabeth 1 1 0
## femrepo maletype
## 1 nonPOP unflanged
## 2 nonPOP unflanged
## 3 nonPOP flanged
## 4 nonPOP unflanged
## 5 nonPOP unflanged
## 6 nonPOP flangedsummary(d3)## Name2 SocialInteractionBinary MaleInteractionBinary MatingBinary
## Marissa :72 : 0 : 0 : 8
## Elizabeth:29 0: 0 0: 0 0:117
## Kristen :11 1:153 1:153 1: 28
## Zarina :11
## Kayla : 8
## Beth : 6
## (Other) :16
## femrepo maletype
## Length:153 Length:153
## Class :character Class :character
## Mode :character Mode :character
##
##
##
## Here, the fixed effects are the female reproductive status and male type. The random effect is the female ID.
full <- glmer(MatingBinary ~ femrepo + maletype + (1|Name2), data = d3, family = binomial(link = "logit"))## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: large eigenvalue ratio
## - Rescale variables?summary(full)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: MatingBinary ~ femrepo + maletype + (1 | Name2)
## Data: d3
##
## AIC BIC logLik deviance df.resid
## 62.4 77.5 -26.2 52.4 148
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -7.7723 0.1287 0.1611 0.2046 0.9443
##
## Random effects:
## Groups Name Variance Std.Dev.
## Name2 (Intercept) 2.537 1.593
## Number of obs: 153, groups: Name2, 15
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.4795 1.0225 3.403 0.000667 ***
## femrepoPOP 23.4004 647.6345 0.036 0.971177
## femrepopreg -1.1272 1.1257 -1.001 0.316696
## maletypeunflanged -0.5299 0.9946 -0.533 0.594193
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) fmrPOP fmrppr
## femrepoPOP 0.000
## femrepopreg -0.520 0.000
## mltypnflngd -0.523 0.000 0.192
## convergence code: 0
## Model is nearly unidentifiable: large eigenvalue ratio
## - Rescale variables?Knott et al. (2010) found that the distribution of matings was strongly predicted by the interaction of male type and female reproductive status. They found the Chi square for global effect = 425, d.f = 2, p < 0.001